An Approach of Artificial Intelligence Application for

Laboratory Tests Evaluation

Ş.l.univ.dr.ing. Corina SĂVULESCU

University of Piteşti

The principal domains where GA The principal domains where GA have successfully applied to have successfully applied to optimization problemsoptimization problems

function optimizationfunction optimization image processingimage processing classification and machine learningclassification and machine learning training of neural networkstraining of neural networks systems’ controlsystems’ control

Why using a GAWhy using a GA??

are stochastic algorithmsare stochastic algorithms use a vocabulary borrowed from natural use a vocabulary borrowed from natural

geneticsgenetics are more robust than existing directed search are more robust than existing directed search

methodsmethods maintain a population of potential solutionsmaintain a population of potential solutions the structure of a simple GA is the same as the structure of a simple GA is the same as

the structure of any evolution programthe structure of any evolution program

A GA for a particular problem must A GA for a particular problem must have the following five have the following five components:components:

a genetic representation for potential a genetic representation for potential solutions to the problemsolutions to the problem

a way to create an initial population of a way to create an initial population of potential solutionspotential solutions

an evaluation function that plays the role an evaluation function that plays the role of environment rating solution in term of of environment rating solution in term of their “fitness”their “fitness”

a genetic operator that alter composition a genetic operator that alter composition of childrenof children

a set of values for various parameters that a set of values for various parameters that the genetic algorithm usesthe genetic algorithm uses

GA’s principles

N individuals

N individuals

N individuals

N individuals

Generation 3

Generation 2

Generation 1

Generation 0

Fitness

The structure of the chosen genetic algorithm

Generation of initial Generation of initial population P(t)population P(t)

Step 1:

The structure of the chosen genetic algorithm

The evaluation function is applied for each The evaluation function is applied for each chromosome of the P(t) population, chromosome of the P(t) population, determining their fitness valuesdetermining their fitness values

Step 2:

S =

n

iii yxf

1

)(

Sf /1

The structure of the chosen genetic algorithm

The population's chromosomes are The population's chromosomes are sorted based on their fitness value sorted based on their fitness value determined during the previous stepdetermined during the previous step

Step 3:

The structure of the chosen genetic algorithm

The best chromosomes are selected, and The best chromosomes are selected, and they will be placed unconditionally in the they will be placed unconditionally in the next population P(t+1)next population P(t+1)

Step 4:

50 % 30 %

15 %

5 %

The structure of the chosen genetic algorithm

The chromosomes that are object to the crossover operator are then selected

Step 5:

2/3

1/3

5/6

2/3

5/6

13/6

3/2

1

8x

N = 8

The structure of the chosen genetic algorithm

The descendants from the previous The descendants from the previous step are subject to the mutation step are subject to the mutation operator, resulting new members for operator, resulting new members for the P(t+1) populationthe P(t+1) population

Step 6:

The structure of the chosen genetic algorithm

The population P(t+1) is completed with The population P(t+1) is completed with individuals selected randomly from the individuals selected randomly from the P(t) populationP(t) population

Step 7:

The application description

Fig. 1 – System's index response Fig. 1 – System's index response

Results of the system identification

Original modelOriginal model 0.6 2.5

Model identified Model identified without noisewithout noise

0.61 2.59

Model identified Model identified with noisewith noise

0.65 2.79

nωξ (rad/sec)

Where are the function’s parameters:n

ωξ

)21sin(21

1)(

tn

tne

ty

Identified system's response

The application of the genetic algorithm in electrophoresis tests

Positioning the agarose gel

The application of serum on the agarose gel

The electrophoresis machine

Drying incubator

An example of results using the agarose gel

The applications of GA to the electrophoresis tests

Application of the genetic algorithm in electrophoresis tests

The results obtained from using a GA from the same example

The results obtained from using a GA from the same example

The test result

Conclusions

This application is an alternative method This application is an alternative method for evaluation of the laboratory tests (in for evaluation of the laboratory tests (in special electrophoresis tests), using special electrophoresis tests), using artificial intelligence.artificial intelligence.

The main advantage of this method is The main advantage of this method is the need of minimal medical knowledge. the need of minimal medical knowledge. Therefore, GA implementation is an Therefore, GA implementation is an instrument easy to use by low/medium instrument easy to use by low/medium trained personnel, offering tests results trained personnel, offering tests results quickly and clearly.quickly and clearly.