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Data Mining Technology Enabled Anti Retroviral Therapy (ART) for HIV Positive Patients in Gondar University Hospital, Ethiopia



Tamrat Delessa Chala*



Department of Computer Science, College of Computing and Informatics, Haramaya University, Ethiopia



Tamrat Delessa Chala - E-mail: tamedase@gmail.com;*Corresponding author


Article Type

Research Article



Received October 9, 2019; Revised November 28, 2019; Accepted December 7, 2019; Published December 8, 2019



It is of interest to discuss the feasibility for data mining technology enabled antiretroviral therapy (ART) for HIV positive patients at the University of Gondar Specialized Teaching Hospital, Ethiopia. The Knowledge Discovery in Databases (KDD), which is in an iterative process where evaluation measures enhanced, is used to prepare the data set for ART in HIV positive patients. A decision tree J48 model that is the implementation of algorithm ID3 (Iterative Dichotomiser 3) developed by the WEKA project team is used in this study. The J48 model was built with pruned and without pruned parameters by selecting two different test modes for 10-fold cross validation with percentage split. Results using J48 pruned decision tree with 10-fold cross validation produces 80.5% prediction precision for ART starter prognosis enabled treatment in clinical settings.



Antiretroviral therapy (ART), KDD methodology, J48 pruned decision tree, ART Starter, WEKA 3.7.



Delessa Chala et al. Bioinformation 15(11): 790-798 (2019)


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.