Aim :Develop prediction model that can be used to facilitate clinicians in targeting patients at high or low risk of mortality.

Method :Logistic RegressionDecision TreeClustering KMeansNeural NetworkHybrid => Neuro Fuzzy, Fuzzy Subtractive Clustering, etc

Comparison of Modelling Technique to Predict Clinical Outcome using Routinely Collected Data

BHOM DatasetModel was built from BHOM dataset, during 12-month study period => 17,417 patients, quarters 1, 2, 3 and 4 (q1,q2,q3,q4).q1 as data training (n1=2257), q2, q3, and q4 are data testing (n2=2335, n3=2361, n4=2544)The fields are :

death - at discharge - F=alive, T =dead (class attribute)age at admissionmode of admission (mostly emergency, but some elective)genderhaemoglobinwhite cell count ureaserum sodiumserum potassiumcreatinineurea / creatinine

Predicted Risk of Mortality for quarter q2,q3 and q4using Logistic Regression

Q2 Q3

Q4

Predicted Risk of Mortality for quarter q2,q3 and q4using Inducer Classifier

Q2 Q3

Q4

Predicted Risk of Mortality for quarter q2,q3 and q4using KMeans Clustering

Q2 Q3

Q4

The Result and Work Plan

Logistic regression is an acceptable method by assessing model performance using techniques designed to test both calibration and discrimination

This involved use of the χ2 test to compare frequency tablesusing the c-index (equivalent to the area under the ROC curve).

Investigating methods at Model Assessment for Inducer Classifier and KMeans Clustering.

Investigating How to Compare the Different Methods