Teferi, Dereje (PhD)Mitike, Getinet (PhD)G/Mariam, Behailu2018-11-232023-11-292018-11-232023-11-292012-06http://etd.aau.edu.et/handle/123456789/14490Background: Many of the reports on HIV/AIDS shows that the number of ART registered patients are increasing from time to time. However those reports show that the increasing of patient’s number, they did not try to make prediction of attributes based on the given attributes more than statistical explanation. This study concerned to use data mining technique on ART data base. The study data was taken from two hospitals of the south west of Ethiopia namely Jimma and Bonga hospitals. Objective: The main objective of the study is to integrate the applicability of data mining techniques on predicting CD4 status of patients following ART in Jimma, and Bonga Hospitals. The main goal of this research is to find the pattern of attributes of the patient in order to build predictive model using data mining techniques. Methodology: The study followed the CRISP-DM data mining methodology, which has six phases called: business understanding, data understanding, data preparation, model building, evaluation and deployment. The study used classification to predict the status of CD4 of patients following ART. J48 is a technique used for building classification and PART is used to compare the result of J48. Findings: The best performance achieved by J48 decision tree algorithm is a generalized decision tree with pruning with reduced attributes. The model classifies instances correctly 88.79% and incorrectly classifies 11.21%. The weighted average precision of the model is 0.88 with recall of 0.89 and ROC area of 0.85. The model has 760 numbers of leaves and 916 tree size. The time taken to build the model is 0.05 seconds. The analysis of this model shows that the model is quit efficient to predict CD4 status of patients following ART. Conclusion: Classification done using J48 decision tree is the best model than PART rule induction algorithm. J48 algorithm is effective to predict the CD4 status of patients following ART. From the model built it is fund that attributes: Eligible reason, ART status, ART start year, OAweight, OAWHO stage, Current regimen, Family planning, Functional status, Marital status, Past ARV are the most determining factors of CD4 status.enPredictive Model For Determining Cd4 StatusThesis