A Machine Learning Approach for Diagnosing Meningococcal

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International Journal of Scientific Research in ___________________________ Research Paper . Computer Science and Engineering Vol.8, Issue.3, pp.13-25, June (2020) E-ISSN: 2320-7639

A Machine Learning Approach for Diagnosing Meningococcal

Meningitis

Solomon Osarumwense Alile1*

, Moses Eromosele Bello2

1,2Computer Science, Faculty of Physical Sciences, University of Benin, Benin City, Nigeria

*Corresponding Author: [email protected], Tel.: +234-7036-4440-86

Available online at: www.isroset.org

Received: 29/May/2020, Accepted: 06/June/2020, Online: 30/June/2020

Abstract— Meningococcal Meningitis is a hazardous sickness brought about by meningococcal microbes called Neisseria

meningitidis which offers ascend to irritation of the meninges which influences people extending from babies, more established youngsters and grown-ups in particular old enough. The manifestations of this sickness are fatigue, nausea,

seizures, vomiting, stiff neck, cold, sleepiness, skin rash, spasm, cough, loss of appetite, fever just to name a yet a couple.

In late past, a few systems have been created to analyze this endemic malady; however they created a great deal of bogus

negative during testing and couldn't distinguish meningococcal meningitis, its overlapping symptoms and serogroup types.

Thus, in this paper, we proposed and simulated a model to anticipate meningococcal meningitis and its serogroup types

utilizing an AI strategy called Bayesian Belief Network. The model was structured utilizing Bayes Server and tried with

data gathered from meningitis medical repository. The model had a 99.99% forecast precision, 97.12% sensitivity of

Meningococcal Meningitis disease, 95.42% sensitivity of Serogroup type A, Serogroup type B, Serogroup type C, and

Neisseria Meningitidis in that order.

Keywords— Meningococcal Meningitis; Bacteria Meningitis; Neisseria Meningitidis; Prediction; Detection; Machine

Learning; Bayesian Belief Network Component

I. INTRODUCTION

The nervous system is an exceptionally unpredictable piece

of the human body that facilitates its activities and tangible

data by transmitting signs to and from various pieces of its

body. In vertebrates, there exist two subsystems which

make the nerbous system; they are to be specific the central

nervous system (CNS) and the peripheral nervous system

(PNS) [1]. The PNS comprises predominantly of nerves,

which are encased packs of the long filaments or axons

that associate the CNS to each other piece of the body. The

CNS includes the brain and spinal cord. Moreover, the

CNS has a layer called the meninges which fill in as the defensive layer for the brain and spinal cord. Due the

sensitivity of this layer, it is dependent upon oddities

which change the smooth activity of this layer. One of such

abnormalities is an irresistible infection called meningitis.

Meningitis is an intense aggravation of the meninges that

covers the brain and spinal cord [2]. On the other hand,

there are a few kinds of organisms that cause meningitis

and are ordered into viruses and bacterium. Viruses that

cause viral meningitis are enteroviruses, herpes simplex

infection, varicella zoster virus, flu infection while

bacterium that causes microbes meningitis are streptococci type B, Neisseria meningitidis, Streptococcus pneumoniae

and Haemophilus Influenzae type B. Of all the previously

mentioned instances of meningitis caused by viruses and

bacteria, Neisseria meningitidis is the most terrifying of

all. Neisseria meningitidis can be recognized from other

bacterium causing meningitis by means of the fast spread of skin rash which is a particular component of

meningococcal meningitis. Meningococcal meningitis is

characterized as a bacterial type of meningitis that is

normally brought about by Neisseria meningitidis,

streptococcus pneumonia and haemophilus influenzae type

B thus causing aggravation or growing of the meninges

which fills in as the defensive layer for the brain and spinal

cord and accompanied with the skin rash. It is additionally

called cerebrospinal meningitis (CSM) [3]. The symptoms

of this disease are fatigue, nausea, seizures, vomiting, stiff

neck, cold, sleepiness, skin rash, spasm, cough, loss of

appetite, fever to give some examples. Be that as it may, the infection influences individuals of any age going from

babies, youngsters, the youthful and old with the hatching

period for this illness in people is supposed to be between

2 to 10 days after contact with the disease.

It was expressed that meningococcal malady is of two

clinical structures to be specific meningococcal

septicaemia which is less normal and meningococcal

meningitis which is the most widely recognized type of

bacteria meningitis that causes plagues. Also,

meningococcal infection is viewed as an endemic and plague ailment, with the two structures causing broad

contamination, passing, tenacious neurological deficiency,

for example, deafness. What's more, different

imperfections of this ailment are loss of limbs, mental

retardation and paralysis. On the other hand, there are three

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Int. J. Sci. Res. in Computer Science and Engineering Vol.8, Issue.3, June 2020


serogroups of meningococcal ailment; they are serogroup

A, B and C individually which are liable for 90% of the

aforementioned ailment. Essentially, the previously

mentioned serogroups have inclination of causing

pestilences and the danger of scourge meningococcal

meningitis changes between the serogroups. Of

quintessence is serogroup A meningococcus since it has been in times passed proven to be the major cause of

epidemic meningococcal disease and has taken

over the continent of Africa for the duration of both

epidemic and endemic era. [4].

World Health Organization (WHO) revealed that the

pervasiveness of meningococcal meningitis in Sub-Sahara

Africa with 30,000 affirmed cases announced every year in

this locale. The sickness influences individuals running

from untimely infants, babies, little children and grown-

ups. The malady is transmitted from individual to

individual through droplets of respiratory or throat secretions from bearer of the infection. The malady could

spread because of close and drawn out contact, for

example, kissing, sneezing and coughing on someone,

smoking and living in close quarters with the carrier [5].

The transmission of the infection can happen in mass get-

togethers, for example, celebrations and religious

pilgrimages. Meningococcal Meningitis as a crippling

infection has represented numerous passings and huge long

haul outcome of any current ailment in patient and stays a

principal worldwide general medical problem with the

seriousness level ranging from asymptomatic, mild, moderate, severe and critical separately [6].

On the other hand, this malady can likewise emerge

because of components viewed as non-irresistible, for

example, utilization of certain prescriptions, malignant

growth and auto-safe ailments just to give some examples.

Furthermore, it was assessed that the developing number of

meningitis cases overall rose from 2.5 million in 1990 to

2.82 million in 2016. Thus in 2015, WHO and Institute for

Health Metrics and Evaluation (IHME) assessed passings

because of meningitis contamination in all ages which

remained at 290,000 and 320,000 separately. Besides, the evaluated number of passings contrasted because of the

trouble in separating between neonatal sepsis and

meningitis with the utilization of oral post-mortem and

absence of diagnostic tests, for example, lumbar puncture

(LP) and cerebrospinal fluid (CSF) examination. In time

past, because of the endemic and pandemic nature of

meningococcal meningitis, clinical techniques have been

used in diagnosing this infection in the method of LP and

CSF separately [7].

Cerebrospinal liquid (CSF) examination is a technique for scanning for conditions that influences the brain and spine.

Besides, it is a sequence of laboratory tests conducted on a

CSF sample [8].

A CSF test is generally recovered by conducting a lumbar

puncture also called spinal tap. Be that as it may, the

investigation of the gathered CSF sample is to measure and

observe in particular fluid pressure, proteins, glucose,

bacteria and viruses just to make reference to yet a couple.

Lumbar Puncture (LP) is characterized as a kind of test

used to diagnose severe infections, for example,

meningitis, disorder of the central nervous system. It is led

at a patient's lower back, which is the lumbar area with the sole point of extracting cerebrospinal fluid (a fluid that

encompasses the brain and spinal cord to shield them from

supporting injury) by means of insertion of needle between

two lumbar bones of the spinal cord [9].

Additionally, clinical work forces regularly use LP to

present sedative prescriptions or chemotherapy drugs into

the cerebrospinal fluid. Be that as it may, a few hours or

days after the lumbar puncture procedure has been

conducted, a patient begins to experience headaches,

nausea, rapid heart rate and low blood pressure. This

delayed consequence of lumbar puncture technique is called post-lumbar puncture cerebral pain.

Also, the technique for diagnosing of meningococcal

meningitis by means of lumbar puncture is very intrusive

and life undermining. Subsequently, there is need to

structure a non-intrusive strategy for diagnosing the

sickness. Consequently, the utilization of AI procedure to

analyze this illness will fill in as an elective which will

help give data that can be used to protect against this sort

of illness before its actual incubation. Previously, a few AI

strategies have been applied in anticipating bacteria meningitis in works of [10, 11, 12, 13,14,15,16 and 17] yet

they produced a great deal of bogus negative during testing

and couldn't distinguish meningococcal meningitis, its

serogroup types and the overlapping symptoms the illness

imparts to other brain furthermore, spinal cord diseases.

In this paper, a directed AI strategy called Bayesian Belief

Network (BBN) was used in diagnosing meningococcal

meningitis, serogroup types and its symptoms. BBN is a

multifaceted probabilistic network that consolidates expert

knowledge and experimental datasets. It plans a route for

cause and effect associations between variables and trains them with likelihood that demonstrates the level where one

variable is probably going to impact another. In this paper,

BBN was our strategy of decision in light of its ability to

make prescient inference.

However, the remainder of the paper is organized as

follows: Section II contains related works on

meningococcal meningitis diagnosis using machine

learning techniques, Section III contains the methodology

utilized in diagnosing meningococcal meningitis which is

supervised machine learning technique called Bayesian Belief Network, Section IV contains the simulation, results

and discussion and Section V concludes research work

with future directions.



II. RELATED WORK

In [10], a system that used an AI approach called

probabilistic neural network (PNN) to separate bacterial

from viral meningitis was developed. The system

characterized between bacterial from viral meningitis and

had a 98% detection precision from dataset. Notwithstanding the high recognition precision, it had the

accompanying issues: the system’ neural network learning

process requires a great deal of time and very costly; it

likewise has a moderate combination speed and less

generalizing performance issue. Moreover, the system

neglected to identify meningococcal meningitis, its

serogroup types and the covering indications the ailment

imparts to other brain and spinal cord diseases.

In [11], an information based system to analyze meningitis

among newborn children and youngsters utilizing fuzzy

cognitive mapping (FCM) was created. The system identified meningitis with 95% exactness, 83.3%

sensitivity and 80% particularity individually. Be that as it

may, the system had the accompanying issues: it couldn't

make bi-directional surmising on the dataset, the

guidelines produced for the FCM are now and again one-

sided and the thinking module proved unable handle issue

of uncertainty. Then again, the system neglected to identify

meningococcal meningitis, its serogroup types and the

covering indications the sickness imparts to other brain and

spinal cord illnesses.

In [12], a system that utilized fuzzy logic and technique for

order performance by similarity to ideal solution (TOPSIS)

to group meningitis in grown-ups was developed. The

proposed system identified meningitis with a high

discovery precision. Be that as it may, the system had the

accompanying issues: the system thinking module couldn't

deal with issue of uncertainty; it was likewise unfit to

make bi-directional inference on the dataset, TOPSIS has

issue of vulnerability in gaining loads just by means of

target techniques. Furthermore, the system neglected to

identify meningococcal meningitis, its serogroup types and

the covering side effects the malady imparts to other brain and spinal cord maladies.

In [13], an expert system that used fuzzy logic in

diagnosing bacterial meningitis and aseptic meningitis was

created. The proposed expert system had 89%

identification precision, 92% particularity and 97%

sensitivity individually. Be that as it may, the system had

the accompanying issues: the system thinking module

couldn't deal with issue of uncertainty; it was additionally

unfit to make bi-directional induction on the dataset.

Moreover, the system neglected to recognize meningococcal meningitis, its serogroup types and the

covering side effects the malady imparts to other brain

also, spinal cord illnesses.

In [14], a specialized system called Bacterial Meningitis

Diagnosis System (BMDS) that utilized fuzzy logic to

analyze bacteria meningitis was designed. BMDS helped

clinical work force to safe existences of patients

languishing from Bacterial Meningitis and had high

discovery exactness. Nonetheless, the system had the

accompanying issues: the system thinking module couldn't

deal with issue of uncertainty; it was likewise incapable to

make bi-directional surmising on the dataset. Moreover, the system neglected to distinguish meningococcal

meningitis, its serogroup types and the covering side

effects the ailment imparts to other brain and spinal cord

maladies.

In [15], a system that utilized artificial neural network

(ANN) to analyze cerebrospinal meningitis (CSM) was

created. The proposed system had 94.7% discovery

exactness. Despite the high recognition precision, it had

the following issues: the system neural network learning

process requires a great deal of time and very costly; a

moderate intermingling speed and less summing up performance issue. Moreover, the system neglected to

recognize cerebrospinal meningitis (CSM), its serogroup

types and the covering indications the malady imparts to

other brain and spinal cord infections.

In [16], a system that used a few Machine Learning (ML)

approaches, for example, Adaptative Boosting (AdaBoost),

Choice Tree, Gradient Boosting, K-Nearest Neighbors

(KNN), Logistic Regression, Random Forest and Support

Vector Machines (SVM) to analyze meningitis was

created. Of the considerable number of methods utilized, Decision Tree calculation had the best identification

precision of 94.56% on preparing information and 96.18%

exactness on testing information, individually. Be that as it

may, regardless of the high recognition precision, it had the

accompanying issues: choice trees are shaky which implies

that a little change in information can prompt a huge

change in the structure of the ideal choice tree created and

they are likewise frequently moderately off base.

Moreover, the system neglected to identify meningococcal

meningitis, its serogroup types and the covering side

effects the illness imparts to other brain and spinal cord

diseases.

In [17], an open and coordinated clinical choice

emotionally supportive network that analyzed meningitis

utilizing tree-based machine learning called Decision Trees

was created. The system determined meningitis to have

88% detection accuracy. Conversely, the system had the

accompanying issues: choice trees are unsteady implying

that a slight change in information can lead to a generally

tremendous change in the structure of the ideal choice tree

made and they are likewise frequently truly erroneous.

Furthermore, the system neglected to distinguish meningococcal meningitis, its serogroup types and the

covering side effects the sickness imparts to other brain

and spinal cord diseases.



III. METHODOLOGY

Machine learning is a lot of strategies for producing

models that delineate or forecast utilizing data or past

experience. Be that as it may, there are a few sorts of AI

namely Supervised Learning: it trains data and

incorporates wanted outputs (for example Bayesian Belief Networks, Neural Networks, Deep learning and so on.),

Unsupervised Learning: it trains data and does exclude

wanted yields (for example Grouping, Dimensionality

Reduction), Semi-Supervised Learning: it trains data and

incorporates hardly any ideal outcome and Reinforcement

Learning: it gains from succession of activities (Temporal

Difference Learning, Q-learning) [18]. In this paper, we

expect to utilize managed AI method called Bayesian

Belief Network because of its prescient ability dependent

on past experience and example information available to its

during training and testing of observed datasets.

Bayesian Belief Network (BBN) is directed acyclic

graphical model that utilizes likelihood to show conditional

dependencies that prevails among nodes on a graph [19]. It

is a complex probabilistic system that blends expert

information and exploratory datasets. It designs out course

of circumstances and logical results connections between

factors and encodes them with probability that signify the

amount wherein one variable is plausible to impact

another. Furthermore, Bayesian Belief Network strives on

the Bayes hypothesis which is relies on probability.

The Bayes hypothesis is represented in the mathematical

equation below:

P(a|b) = P(b|a)P(a) P(b)

Where,

P(a) is the probability of event “a” happening without any

information about event “b”. It is called the “Prior”.

P(a/b) is the conditional probability of event “a” happening

given that event “b” has already occurred. It is otherwise

called the “Posterior”.

P(b/a) is the conditional probability of event “b” happening

given that event “a” has already occurred. It is called the “Likelihood”.

P(b) is the probability of event “b” happening without any

information about event “a”. It is called the “Marginal

Likelihood”.

The Naive Bayes classifiers are regularly spoken to as a

sort of directed acyclic graph (DAG). The Directed

Acyclic Graph (DAG) comprises of vertices representing

random variables and arrows connecting pairs of nodes.

Figure 1 shows a pictorial representation of a Bayesian

Belief Network.

Figure 1. A Pictorial Representation of a Bayesian Belief

Network

A few preferences of this model are: it is very speedy in

making inferences, the resulting probabilities are quite

easy to decipher, the learning algorithm is very

straightforward and the model sufficiently consolidates

with utility functions to make optimal inferences. In this

paper, we expect to recognize meningococcal meningitis,

serogroup types also, its symptoms utilizing a managed AI

procedure called Bayesian Belief Network (BBN). A

model comprising of 67 nodes where a few nodes speak to a type of ailment illness or elements that impact diagnosis

of meningococcal meningitis, serogroup types and its side

effects will be planned utilizing Bayes Server. A

meningococcal meningitis dataset will be utilized to train

and test the system. Utilizing the Pareto Principle, 80% of

the dataset will be utilized to prepare the model while the

remainder will be utilized in testing the model. The point

of the model is to accomplish an elevated level of

identification exactness with the utilization of the covering

indications and serogroup sorts of meningococcal

meningitis.

IV. SIMULATION, RESULTS AND DISCUSSION

The simulation was performed utilizing a meningitis

dataset in training, testing and predicting meningococcal

meningitis which was retrieved from [20]. In addition,

previews of used dataset, designed BBN model for

identifying meningococcal meningitis, its symptoms and

serogroup types, BBN model convergence chart,

loglikelihood batch query chart, feature importance of

nodes chart, in-sample anomaly detection chart, mesh query plot of neisseria meningitidis being the cause of

meningococcal meningitis infection, likelihood plots of

serogroups, neisseria meningitidis, bacteria meningitis and

neisseria meningitidis (joined) causing epidemic

meningococcal meningitis, loglikelihood graph for

detecting meningococcal meningitis and likelihood against

loglikelihood plot for predicting meningococcal meningitis

were taken during the simulation process which appears

beneath in figures 2, 3, 4, 5, 6, 7, 8,9,10,11,12,13,14 and

15 respectively with the results discussed underneath the

diagrams. Nevertheless, the utilized dataset include mix of

ailments and parts thought about in the identification of meningococcal meningitis signifying 67 with each sickness

and factor having a value which addresses the probability

Cj

P=(B1|C1

)

P=(B1|C1

)

P=(B1|C1

)



of such ailment malady and factor causing meningococcal

meningitis disease. The ailments and components are: Age

< 1, Age > 1, agitation, anemia, bacteria meningitis,

behavior changes, blue lips, body ache, cerebral malaria,

chest pain, cold, cognitive impairment, coma, continuous

fever, cough, delirium, dehydration, disturbed sleep habits,

emotional disturbances, fatigue, fever, haemophilus influenza, hallucinations, headache, hyperactive delirium,

hypoactive delirium, impaired consciousness, incubation

period (2 to 10 days), influenza, intermittent fever, joint

pain, loss of appetite, malaria fever, meningococcal

disease, meningococcal meningitis, meningococcal

septicaemia, mild jaundice, mixed delirium, moaning,

muscle pain, nausea, nasal congestion, neisseria

meningitidis (NM), neutropenic fever, pel-ebstein fever,

pneumonia, quartan fever, quotidian fever, reduced

awareness of environment, remittent fever, restlessness,

runny nose, seizures, serogroup A, serogroup B, serogroup

C, skin rash, sleepiness, sluggishness, spasm, sore throat, stiff neck, streptococcus pneumonia, sweating, tertian

fever, typhoid fever, vomiting and yellow fever

respectively.

Figure 2 beneath shows a sample the dataset.

Figure 2. Snapshot of Dataset

The Bayesian Belief Network model was structured

utilizing Bayes-Server platform. The Bayesian Belief

Network (BBN) for foreseeing meningococcal meningitis,

its serogroup types and symptoms were structured with the

end goal that the nodes on the system are connected

dependent on the likelihood of a malady sickness coming

about to another and factor impacting another factor. In our

model for a case to be signified as meningococcal meningitis, the sicknesses, disease causing agents and

different components taken into cognizance in the

diagnosis of meningococcal meningitis are: Age < 1, Age

> 1, agitation, anemia, bacteria meningitis, behavior

changes, blue lips, body ache, cerebral malaria, chest pain,

cold, cognitive impairment, coma, continuous fever,

cough, delirium, dehydration, disturbed sleep habits,

emotional disturbances, fatigue, fever, haemophilus

influenza, hallucinations, headache, hyperactive delirium,

hypoactive delirium, impaired consciousness, incubation

period (2 to 10 days), influenza, intermittent fever, joint pain, loss of appetite, malaria fever, meningococcal

disease, meningococcal septicaemia, mild jaundice, mixed

delirium, moaning, muscle pain, nausea, nasal congestion,

neisseria meningitides (NS), neutropenic fever, pel-ebstein

fever, pneumonia, quartan fever, quotidian fever, reduced

awareness of environment, remittent fever, restlessness,


C, skin rash, sleepiness, sluggishness, spasm, sore throat, stiff neck, streptococcus pneumonia, sweating, tertian

fever, typhoid fever, vomiting and yellow fever

respectively.

Figure 3 shows the BBN model for detecting

meningococcal meningitis, serogroup types and its

symptoms.

Figure 3. Bayesian Belief Network Model for Detecting

Meningococcal Meningitis, Its Serogroup Types and Symptoms.

So, to mathematically represent our model we have: Meningococcal Meningitis

(2)

Where,

Disease: Node with a Disease Ailment

Parents (Diseasei) = Nodes that converge on Disease Ailmenti..

The dataset was used to train and test the model. Upon

completion of training and testing the BBN model, the test

data converged at time series 2. The loglikelihood value

for each case was recorded. Figure 4 shows the BBN

model convergence of meningococcal meningitis and its

symptoms at Iteration Count 2.

Figure 4. Bayesian Belief Network Model for Detecting

Meningococcal Meningitis and its Serogroup Types Converging at Time Series 2.

= P(Diseasei )i = 1

67

% | Parentsi (Diseasei )



Figures 5, 6, 7, 8, 9, 10, 11, 12 ,13, 14 and 15 shows log

likelihood batch query chart for predicting meningococcal

meningitis with its serogroup types and symptoms, feature

importance chart for nodes in the model, the in-sample

anomaly detection chart, the mesh query plot for the

loglikelihood of neisseria meningitis being the reason for a

meningococcal meningitis infection, the likelihood plot showing relation of serogroup A leading to a

meningococcal meningitis infection case and its

probabilities; the likelihood plot showing relation of

serogroup B leading to meningococcal meningitis infection

case and its probabilities; the likelihood plot showing

relation of serogroup C leading to meningococcal

meningitis infection case and its probabilities; the

probability plot indicating connection of how contact with

neissera meningitis prompts meningococcal meningitis and

Its probabilities in the BBN Model, the likelihood plot

showing relation of neisseria meningitis and bacteria

meningitis (joined) leading to a meningococcal meningitis infection case and its probabilities and the loglikelihood

graph for detecting meningococcal meningitis with its

symptoms and likelihood against loglikelihood for

predicting meningococcal meningitis with its symptoms

respectively. The outcome generated from the simulation

indicated that the system had the option to anticipate

99.99% meningococcal meningitis on the dataset precisely

and it had a loglikelihood of 76 on the test dataset. The

figure 5 below shows the loglikelihood batch query chart

for predicting meningococcal meningitis with its serogroup

types and symptoms.

Figure 5. The Loglikelihood Chart Batch Query for Predicting Meningococcal Meningitis, Symptoms and its Serogroup Types

This loglikelihood chart batch shows the result of the test

data. Here, 50 experimental cases were conducted and the

analysis of the result generated from the test data is shown

below:

In Experiment 1: The value of Predict(Meningococcal

Meningitis) was 0.774 compared to 0.774011247, Predict

(Neisssera Meningitidis) was 0.253 compared to

0.253001682, Predict(Serogroup A) was 0.411 compared

to 0.41102189, Predict(Serogroup B) was 0.0964

compared to 0.096404102, Predict(Serogroup C) was

0.936 compared to 0.93570102 in Experiment 1.



(Neisssera Meningitidis) was 0.799 compared to 0.79900015, Predict(Serogroup A) was 0.362 compared to

0.3620289, Predict(Serogroup B) was 0.94 compared to

0.94001890, Predict(Serogroup C) was 0.489 compared to

0.48901001 in Experiment 2.



(Neisssera Meningitidis) was 0.13 compared to

0.130022100, Predict(Serogroup A) was 0.556 compared

to 0.5560021, Predict(Serogroup B) was 0.504 compared

to 0.504001014, Predict(Serogroup C) was 0.489

compared to 0.4890102109 in Experiment 3.

Furthermore, this experiment continues up to Experiment

number 50. Hence, the system results showed a 0.001

value difference between the prediction results and original

test data of 100% resulting to 99% prediction accuracy.

The figure 6 below shows the feature importance chart for

nodes in the Bayesian Belief Network model.

Figure 6: The Feature Importance Chart for Nodes in the BBN

Model

The feature importance chart shows p-value of the variable

(nodes), feature and mutual information in reference to the

Meningococcal Meningitis Node.

The p-value signifies the likelihood (probability) of the

nodes being the cause of a Meningococcal Meningitis

infection.

The Feature box is checked if that particular node is fully

involved in the cause of a Meningococcal Meningitis

infection.

The Mutual Information shows the relationship with nodes directly connected to one another (i.e. in this case the direct

relationship of the nodes with the Meningococcal

Meningitis) and assigned a value.

The Significance Level signifies the margin of error in the

detection of Meningococcal Meningitis and its symptoms.



The figure 7 below shows the in-sample anomaly detection

chart for the Bayesian Belief Network Model.

Figure 7. The In-Sample Anomaly Detection Chart Chart for

Nodes in the BBN Model

The in-sample anomaly detection chart shows 50

experimental results of detecting Meningococcal

Meningitis. Each Case is assigned an ID(Identification

value) which is the value of the Predict(Meningococcal

Meningitis), Predict (Neissera Meningitidis), Predict(Serogroup A), Predict(Serogroup B) and

Predict(Serogroup C) in figure 5 above. The IsAnomaly

checkbox is checked to identify that each case is a

confirmed case of Meningococcal Meningitis infection.

The 50 cases of Meningococcal Meningitis has a case

count value of 22.801 (weighted) which signifies the

importance of the cases leading to a Meningococcal

Meningitis infection and 50 case count value signifies the

number of cases in the pool of data available to the system

for detection of Meningococcal Meningitis in the dataset

pool. The tolerance is the margin of error that could be encoutered as regards to the detection of the

Meningococcal Meningitis and its symptoms.

The figure 8 below shows the mesh query plot for the

loglikelihood of Neisseria Meningitidis being the reason

for a Meningococcal Meningitis Infection.

Figure 8: The Mesh Query Plot for the Loglikelihood of Neisseria Meningitidis Being The Reason For A Meningococcal Meningitis

Infection.

The mesh query plot shows the loglikelihood/likelihood of

a node in this case Neisseria Meningitidis being a cause of

a confirmed Meningococcal Meningitis infection. The

Node Neisseria Meningitidis is plotted on the Y-axis and

the other node Meningococcal Meningitis plotted along the

X-axis.

In this context, the Red contour signifies the likelihood of

Neisseria Meningitidis being a major of cause of

Meningococcal Meningitis infection with the contour

ranging from interval (-1.750 to -1.600) on the Y-axis and

interval (-1.700 to1.700) on the X-axis.

The Yellow contour shows the loglikelihood of a Neisseria

Meningitidis being the reason of a Meningococcal

Meningitis infection with the contour ranging from interval

(0.850 to -0.550) on the Y-axis and interval (-1.200 to

0.940) on the X-axis.

Figure 9 below shows the probability plot showing relation of serogroup type A as the cause of epidemic

Meningococcal Meningitis and its probabilities in the BBN

Model.

Figure 9. The Probability Plot Showing Relation of Serogroup

type A as the Cause of Epidemic Meningococcal Meningitis And Its Probabilities In The BBN Model

The probability plot shows the chance of serogroup type A

being the reason for pestilence meningococcal meningitis.

In this plot, 50 exploratory cases were contemplated with

each hued point in the chart named a case and allotted a

likelihood value which is positioned on the right of the

graph. The serogroup type A on the Y-axis is plotted

against Meningococcal Meningitis on the X-axis. Be that as it may, from this diagram, there are five diagnostic

classes of Serogroup type A infection which our system

was ready to recognize; they are asymptomatic, mild,

moderate, severe, and critical classes individually.

Asymptomatic Class: This class ranges from 0 to 0.2 on Y-

axis and 0.000153 to 1.002 on X-axis. This district has 12

hued focuses (cases). This means the 12 colored focuses in

this locale speak to 12 instances of no serogroup type A

disease at all, consequently this class of patients are

arranged as being Asymptomatic.



Mild Class: This class ranges from 0.2 to 0.4 on Y-axis and

0.000153 to 1.002 on X-axis. This locale has 11 colored

focuses (cases). This connotes the 11 hued focuses in this

locale speak to 11 instances of patients with serogroup type

A disease with the severity level affirmed as being Mild.

Moderate Class: This class ranges from 0.4 to 0.6 on Y-

axis and 0.000153 to 1.002 on X-axis. This locale has 11 hued focuses (cases).This implies that the 11 hued focuses

in this region speak to 11 instances of patients with

serogroup type A disease with the seriousness level

classified as being Moderate.

Serious Case: This class ranges from 0.6 to 0.8 on Y-axis

and 0.000153 to 1.002 on X-axis. This region has 9 shaded

focuses (cases). This means the 9 colored focuses in this


A infection with the seriousness level ordered as being

Severe.

Critical Class: these level extents from 0.8 to 1 on Y-axis and 0.000153 to 1.002 on X-axis. This area has 7 colored



A infection with the seriousness level ordered as being

Critical.

All 50 cases in figure 9 had a probability value less than 1;

with the most elevated likelihood estimation of serogroup

type A infectivity being the significant reason for epidemic

Meningococcal Meningitis infection answered to be

0.954219893465408 which is under 1. Of the 50 test cases, the system anticipated all instances of

serogroup type A disease running from asymptomatic,

mild, moderate, serious, and critical classes accurately

from the test data with 95.42% sensitivity of serogroup

type A infection.

Figure 10 beneath shows the probability plot

demonstrating connection of serogroup type B as the

reason for plague meningococcal meningitis and Its

probabilities In The BBN Model.

Figure 10: The Probability Plot Demonstrating Connection of Serogroup type B As The Reason For Plague Meningococcal

Meningitis And Its Probabilities In The BBN Model.

The probability plot shows the chance of serogroup type B being the reason for pestilence meningococcal meningitis.

In this plot, 50 trial cases were mulled over with each

colored point in the graph delegated a case and allocated a

likelihood value which is positioned on the right of the

chart. The serogroup type B on the Y-axis is plotted

against Meningococcal Meningitis on the X-axis. Be that

as it may, from this diagram, there are five symptomatic

classes of serogroup type B contamination which our

system was ready to distinguish; they are asymptomatic, mild, moderate, serious, and critical classes individually.


axis and 0.000153 to 1.002 on X-axis. This locale has 12

colored focuses (cases). This implies the 12 hued focuses

in this locale speak to 12 instances of no serogroup type B

infection at all, subsequently these classifications of

patients are ordered as being Asymptomatic.


0.000153 to 1.002 on X-axis. This district has 11 hued

focuses (cases). This connotes the 11 tinted focuses in this

locale speak to 11 instances of patients with serogroup type B disease with the seriousness level classified as being

Mild.



colored focuses (cases).This implies that the 11 hued

focuses in this locale speak to 11 instances of patients with

serogroup type B contagion with the seriousness level

arranged as being Moderate.

Severe Case: This class ranges from 0.6 to 0.8 on Y-axis and 0.000153 to 1.002 on X-axis. This locale has 9 colored


area speak to 9 instances of patients with serogroup type B

infectivity with the seriousness level arranged as being

Severe.

Critical Class: This level reaches from 0.8 to 1 on Y-axis

and 0.000153 to 1.002 on X-axis. This locale has 7 hued

focuses (cases). This implies the 7 hued focuses in this area

speak to 7 instances of patients with Serogroup type B

disease with the seriousness level sorted as being Critical.

All 50 cases in figure 10 had a probability value less than 1; with the most noteworthy likelihood estimation of

serogroup type B contagion being the significant reason for

epidemic Meningococcal Meningitis Infection answered to

be 0.954219893465408 which is under 1.

Of the 50 test cases, the system anticipated all instances of

serogroup type B disease going from asymptomatic, mild,

moderate, serious, and critical classes accurately from the

test data with 95.42% sensitivity of serogroup type B

Infection.

Figure 11 beneath shows the probability plot

demonstrating connection of serogroup type C as the

reason for pestilence Meningococcal Meningitis and its

probabilities in the BBN Model.



Figure 11. The Probability Plot Showing Relation of Serogroup type C as the Cause of Epidemic Meningococcal Meningitis And

Its Probabilities In The BBN Model

The probability plot shows the chance of serogroup type C

being the reason for plague Meningococcal Meningitis. In

this plot, 50 exploratory cases were mulled over with each

hued point in the graph named a case and alloted a

likelihood which is positioned on the right of the plot. The

serogroup type C on the Y-axis is plotted against

Meningococcal Meningitis on the X-axis.

Nonetheless, from this graph, there are five diagnostic

classes of serogroup type C disease which our system was

ready to identify; they are asymptomatic, mild, moderate,

severe, and critical classes separately.


axis and 0.000153 to 1.002 on X-axis. This area has 10

hued focuses (cases). This means the 10 hued focuses in

this locale speak to 10 instances of no serogroup type C

disease at all, henceforth this classification of patients are arranged as being Asymptomatic.


0.000153 to 1.002 on X-axis. This locale has 11 colored

focuses (cases). This implies the 11 hued focuses in this


C disease with the seriousness level classified as being

Mild.



colored focuses (cases).This implies that the 8 hued focuses in this area speak to 8 instances of patients with

serogroup type C contagion with the seriousness level

regarded as being Moderate.

Severe Case: This class ranges from 0.6 to 0.8 on Y-axis

and 0.000153 to 1.002 on X-axis. This area has 6 colored

focuses (cases). This means the 6 hued focuses in this

region speak to 6 instances of patients with serogroup type

B contagion with the severity level categorized as being

Severe.


and 0.000153 to 1.002 on X-axis. This area has 15 shaded


locale speak to 15 instances of patients with Serogroup

type C infectivity with the seriousness level arranged as

being Critical.

All 50 cases in figure 11 had a probability value less than

1; with the most noteworthy likelihood estimation of

serogroup type C disease being the significant reason for epidemic Meningococcal Meningitis Infection answered to

be 0.954219893465408 which is under 1.

Of the 50 trial cases, the system anticipated all instances of

serogroup type C disease extending from asymptomatic,

mild, moderate, serious, and critical classes effectively

from the test data with 95.42% sensitivity of serogroup

type C Infection.

Figure 12 underneath shows the probability plot indicating

connection of how contact with Neissera Meningitis

prompts Meningococcal Meningitis and its probabilities in the BBN Model.

Figure 12. The Probability Plot Showing Relation of how contact with Neissera Meningitis Leading to a Meningococcal Meningitis

Infection Case And Its Probabilities In The BBN Model

The probability plot shows the chance of how contact with Neissera Meningitis drives a Meningococcal Meningitis

disease case. In this plot, 50 trial cases were mulled over

with each shaded point in the diagram delegated a case and

allocated a likelihood which is positioned on the privilege

of the diagram. The Predict(Neissera Meningitis) on the Y-

axis is plotted against Meningococcal Meningitis on the X-

axis.

Notwithstanding, from this diagram, there are five

diagnostic classes of Neissera Meningitis cases which our

system was capable to identify; they are asymptomatic,

mild, moderate, severe, and critical classes separately.


axis and 0.00153 to 1.002 on X-axis. This area has 7

colored focuses (cases). This connotes the 7 hued focuses

in this district speak to 7 instances of no Neissera

Meningitis contamination at all, thus this classification of

patients are ordered as being Asymptomatic.




0.00153 to 1.002 on X-axis. This district has 9 tinted

focuses (cases). This connotes the 9 shaded focuses in this

locale speak to 9 instances of patients with Neissera

Meningitis disease with the seriousness level recognized as

being Mild.



hued focuses (cases).This means that the 10 hued focuses

in this area speak to 10 instances of patients with Neissera

Meningitis disease with the seriousness level classified as

being Moderate.


and 0.00153 to 1.002 on X-axis. This area has 7 shaded


district speak to 7 instances of patients with Neissera

Meningitis contamination with the severity level regarded as being Severe.


and 0.00153 to 1.002 on X-axis. This locale has 15 shaded

focuses (cases). This implies the 15 shaded focuses in this

area speak to 15 instances of patients with Neissera

Meningitis disease with the seriousness level classified as

being Critical.

All 50 cases in figure 12 had a probability value under 1;

with the most elevated likelihood estimation of Neissera Meningitis causing a Meningococcal Meningitis Infection

answered to be 0.954219893465408 which is under 1.

Of the 50 trial cases, the system anticipated all instances of

Neissera Meningitis going from asymptomatic, mild,

moderate, severe, and critical classes accurately from the

test data with 95.42% sensitivity of Neissera Meningitis

Infection.

Figure 13 underneath shows the probability plot indicating

connection of how contact with consolidated bacteria

meningitis and neissera meningitis prompts Meningococcal

Meningitis and its probabilities In The BBN Model.

Figure 13. The Likelihood Plot Showing Relation of how contact with Bacteria Meningitis and Neissera Meningitis Consolidated Leading to a Meningococcal Meningitis Infection Case And Its

Probabilities In The BBN Model

The probability plot shows the chance of how contact with

blend of Bacteria Meningitis and Neissera Meningitis leads

a Meningococcal Meningitis infection case. In this plot, 50

test cases were taken into thought with each hued point in

the chart delegated a case and allotted a likelihood which is

positioned on the right of the plot. The blend of Bacteria

Meningitis and Neissera Meningitis on the Y-axis is plotted against Meningococcal Meningitis on the X-axis.

Be that as it may, from this chart, there are five diagnostic

classes of the blend of Bacteria Meningitis and Neissera

Meningitis cases which our system had the option to

recognize; they are asymptomatic, mild, moderate, severe

and critical classes individually.


axis and 0.02359 to 1.024 on X-axis. This area has 5 hued

focuses (cases). This means the 5 hued focuses in this area

speak to 5 instances of no blend of Bacteria Meningitis and

Neissera Meningitis contamination at all, thus this classification of patients are identified as being

Asymptomatic.


0.02359 to 1.024 on X-axis. This area has 11 colored

focuses (cases). This means the 11 hued focuses in this

locale speak to 11 instances of patients with joint Bacteria

Meningitis and Neissera Meningitis infection with the

seriousness level arranged as being Mild.

Moderate Class: This class ranges from 0.4 to 0.6 on Y-axis and 0.02359 to 1.024 on X-axis. This locale has 10

colored focuses (cases).This implies that the 10 hued

focuses in this locale speak to 10 instances of patients with

consolidated Bacteria Meningitis and Neissera Meningitis

infectivity with the seriousness level recognized as being

Moderate.


and 0.02359 to 1.024 on X-axis. This locale has 12 colored


area speak to 12 cases of patients with consolidated

Bacteria Meningitis and Neissera Meningitis contamination with the seriousness level classified as being

Severe.

Critical Class: This level extent from 0.8 to 1 on Y-axis

and 0.02359 to 1.024 on X-axis. This district has 12

colored focuses (cases). This means the 12 hued focuses in

this area speak to 12 instances of patients with joint

Bacteria Meningitis and Neissera Meningitis infection with

the seriousness level identified as being Critical.

All 50 cases in figure 13 had a probability value less than 1; with the most noteworthy likelihood estimation of joint

Bacteria Meningitis and Neissera Meningitis contagion

causing a Meningococcal Meningitis Infection answered to

be 0.971170884061489 which is less than 1.

Of the 50 exploratory cases, the system anticipated all

instances of consolidated Bacteria Meningitis and Neissera



Meningitis prompting Meningococcal Meningitis disease

case going from asymptomatic, mild, moderate, severe and

critical classes effectively from the test data with 97.11%

sensitivity of the blend of Bacteria Meningitis and Neissera

Meningitis being the reason for a meningococcal

meningitis infection.

Besides, with the severity level of Meningococcal

Meningtitis, Neissera Meningitis, consolidated Bacteria

Meningitis and Neissera Meningitis, Serogroup Type A,B

and C already attained, we intend to plot the graph for the

loglikelihood graph for detecting Meningococcal

Meningitis with its indications; likelihood against

loglikelihood chart for predicting Meningococcal

Meningitis and also unravel the loglikelihood value for

detecting Meningococcal Meningitis; and prediction

accuracy of the BBN model which will be discussed in

figure 14 and 15 below.

The figure 14 below shows the loglikelihood graph for

detecting Meningococcal Meningitis with its indications.

Figure 14. The Loglikelihood Graph for Detecting

Meningococcal Meningitis with its Symptoms

This loglikelihood chart for identifying Meningococcal

Meningitis shows the remaining qualities on the vertical

axis plotted against the loglikelihood values on the flat axis

which are autonomous factors. A residual value is an

evaluation of how much a relapse line vertically misses an

information point. Regression lines are the standout attack of a lot of information. The lines are delegated normal with

a couple of information focuses fitting the line while others

miss the line. In this chart, 50 trial cases were directed

which brought about the estimations of 76, 75.85, 75.03,

74.25, 74.15,74.… … .. what's more, 69.43 individually.

Ideally, remaining qualities ought to be uniformly and

arbitrarily separated around the level lines. Fundamentally,

watching the system' trial results esteems acquired from

the level lines on the diagram, it tends to be seen that the

point where the most noteworthy residual value and the

loglikelihood autonomous variable achieved meets at - 75.41 on X-axis and 76 on Y-axis, with 78 being the most

extreme worth that can be accomplished by the system.

Henceforth, the Loglikelihood value of identifying

Meningococcal Meningitis with its Symptoms is 76.

Figure 15 shows the likelihood against loglikelihood for

anticipating meningococcal meningitis with its indications.

Figure 15. The Likelihood against Loglikelihood Graph for Predicting Meningococcal Meningitis with its Symptoms

This probability against loglikelihood plot for foreseeing

Meningococcal Meningitis shows the remaining qualities

(Probability) on the vertical axis plotted against the Loglikelihood esteems on the horizontal axis which are

free factors.

A residual (Likelihood) esteem is an evaluation of how

much a regression line vertically misses an information

point. It appears the likelihood of an event (Meningococcal

Meningitis) happening with the probabilistic qualities

somewhere in the range of 0 and 1.

Regression lines are the standout attack of a lot of

information. The lines are delegated normal with a couple

of information focuses fitting the line while others miss the

line.

In this diagram, 50 exploratory cases were conducted

which brought about the estimations of 0.9999, 0.9997,

0.9995, 0.9990, 0.8995, 0.8950.…….. furthermore, 0.0003

individually.

Ideally, residual (probability) values ought to be uniformly

and haphazardly dispersed around the level lines.

Basically, watching the system' trial results esteems

acquired from the level lines on the diagram, it can be seen

that the likelihood probability esteem obtained is 0.9999

and loglikelihood free expectation esteem is - 74.41.

Henceforth, in this system, the most noteworthy likelihood

esteem that can be achieved is 1. With 1, being the 100 %

residual (probability) esteem mark, to get our forecast

precision rate, we divide acquired likelihood probability

esteem by most noteworthy likelihood esteem that can be

accomplished and increase by most noteworthy residual

esteem mark, that is 0.9999/1*100% = 99.99% forecast

precision rate on the test data.

Besides, the probability plots brings about figure 9, 10, 11,

12 and 13 demonstrated all classes of seriousness status of



serogroup type (A, B and C), neissera meningitis and

consolidated bacteria meningitis and neissera meningitis

prompting meningoccal meningits disease cases separately

going from asymptomatic, mild, moderate, severe, also,

critical classes individually with their probabilities; figure

14 demonstrated the system loglikelihood estimation of 76

for distinguishing meningococcal meningitis and its indications while the likelihood against loglikelihood

prediction graph of meningococcal meningitis and its side

effects in figure 15 indicated the 99.99% forecast precision

of the system.

In this way, the likelihood of having meningococcal

meningitis infection given there is proof of infirmities and

variables that impact analysis of the aforementioned

ailment is signified as:

P(Meningococcal Meningitis| Age < 1, Age > 1, agitation,

anemia, bacteria meningitis, behavior changes, blue lips,

body ache, cerebral malaria, chest pain, cold, cognitive impairment, coma, continuous fever, cough, delirium,

dehydration, disturbed sleep habits, emotional

disturbances, fatigue, fever, haemophilus influenza,

hallucinations, headache, hyperactive delirium, hypoactive

delirium, impaired consciousness, incubation period (2 to

10 days), influenza, intermittent fever, joint pain, loss of

appetite, malaria fever, meningococcal disease,

meningococcal septicaemia, mild jaundice, mixed

delirium, moaning, muscle pain, nausea, nasal congestion,

neisseria meningitidis, neutropenic fever, pel-ebstein fever,

pneumonia, quartan fever, quotidian fever, reduced awareness of environment, remittent fever, restlessness,


C, skin rash, sleepiness, sluggishness, spasm, sore throat,

stiff neck, streptococcus pneumonia, sweating, tertian

fever, typhoid fever, vomiting and yellow fever =

0.971170884061489 proportional to 97.12%.

From the analysis, it very well may be seen that our model

had a high residual loglikelihood esteem of 76, an overall

forecast exactness of 99.99%; 97.11% sensitivity of

consolidated Neisseria Meningitidis and Bacteria

Meningitis prompting a Meningococcal Meningitis, 95.42%, sensitivity of Serogroup type A, Serogroup type

B, Serogroup type C and Neisseria Meningitidis in that

order.

At long last, looking at the 99.99% forecast precision of

our model with the experiments conducted by [11],[12],

[14],[16],[17] and [18] which had 98%, 95%, 89%, 94.7%,

94.56% and 88%% forecast exactness individually, it is

self-evident our model has a superior forecast exactness.

The higher forecast precision attained by our model could

be due to the range of the dataset utilized in training and testing the model just as its capacity to anticipate the

Meningococcal Meningitis, serogroup types and covering

side effects the ailment imparts to brain and spinal cord

ailments, henceforth helping the high detection exactness

of the aforementioned disease.

V. CONCLUSION AND FUTURE SCOPE

In this study, we used a managed AI strategy called

Bayesian Belief Network (BBN) to create a model for

early diagnosis of meningococcal meningitis disease. The

model was assembled dependent on epidemic and

predictive data of meningitis illness outbreaks in Nigeria. The model depends on 67 factors speaking to indications

and elements that impact the forecast of meningococcal

meningitis cases. Meningococcal Meningitis is an

irresistible perilous illness that is brought about by

neisseria meningitidis bringing about inflammation of the

meninges which is the protective cover for the brain and

spinal cord which is very hard to identify. In ongoing past,

a few clinical and AI methods have been used to diagnose

meningococcal meningitis with the point of lessening the

inauspicious passings of patients because of absence of

early diagnosis of the aforesaid illness an area which

clinical and IT experts are putting frantic efforts to enhance. The model was trained and tested and had an

overall exactness of 99.99% in predicting Meningococcal

Meningitis, its manifestations and serogroup types; 97.12%

sensitivity of consolidated Neisseria Meningitidis and

Bacteria Meningitis prompting a Meningococcal

Meningitis infection, 95.42% sensitivity of Serogroup type

A, Serogroup type B, Serogroup type C and Neisseria

Meningitidis in that order.

We can presume that the results obtained from the system’

inference showcased the model's learning capacity, in spite of the fact that the data can be improved enormously,

particularly for the figure of flare-ups of the

meningococcal meningitis sickness aside from that in this

study, just 1 nation was considered. The figure for Nigeria,

toward the end, additionally indicated empowering results,

regardless of the fact that the conclusion of the

aforementioned infection by the proposed model depended

on the precision of the expectation of infected cases during

the investigation stage.

For future works, there is need to include more data from

nations influenced by the sickness in other to improve the prescient and get ideal outcomes which will be used and

realize improvement in the following areas: forecast of

meningococcal meningitis and serogroup types,

identification of meningococcal meningitis and diagnosis

of brain and spinal cord sicknesses with comparable side

effects as meningococcal meningitis and its serogroup

types.

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Authors Profile

Mr. Solomon Osarumwense Alile obtained a Diploma in

Data Processing degree from University of Benin in 2004,

B.Sc. degree in Computer Science in 2012 from Lagos

StateUniversity (LASU), Ojo, Lagos and M.Sc in

Computer Science from University of Benin in 2019. He

is a Cisco Certified Network Associate (Routing and

Switching) and System Engineer. He is a corporate

member of Nigerian Institution of Professional Engineers

and Scientists (NIPES), and member of the following societies namely: International Computer Science and

Engineering Society (ICSES), Institute For Engineering

Research and Publication (IFERP), International

Association of Engineers (IAENG), International

Association of Engineers Society of Computer Science

(ISCS), International Association of Engineers Society of

Wireless Networks (ISWN), International Association of

Engineers Society of Scientific Computing (ISSC),

International Association of Engineers Society of Internet

Computingand Web Services (ISICWS), International

Association of Engineers Society of Information System

Engineering (ISISE), International Association of Engineers Society of Data Mining (ISDM), International

Association of Engineers Society of Artificial Intelligence

(ISAI), and International Association of Engineers Society

of Software Engineering (ISSE). He has published more

than 10 research papers in reputed international journals

which covers the area of cybersecurity and application of

artificial intelligence in health diagnosis, all of which are

available online. His skills are Machine Learning,

Supervised Learning, Computer Networking, Routing and

Switching and Network Security. His area of interest

includes Information Technology, Machine Learning and Cybersecurity. He is currently conducting research works

in the area of cybersecurity and development of AI Based

Systems.

Mr Moses Eromosele Bello obtained Ordinary National

Diploma in Computer Science from Auchi Polythenic in

2010, Bachelor of Science and Master of Science from

University of Benin, Benin City in year 2014 and 2019

respectively. He is a member of Nigeria Computer Society

(NCS) since 2019. He has published more than 11 research

papers in reputed international journals and are available

online. His skills are Machine Learning, Supervised Learning, Neural Networks, Artificial Intelligence and Java

Programming. His main research work focuses on machine

learning and development of AI Based Systems.

Documents

A Machine Learning Approach for Diagnosing Meningococcal