Abstract:
Since HIV recognized up to July 2007, it has infected close to 70 million people in the world,
and more than 30 million have died due to acquired immunodeficiency syndrome (AIDS).
More than 66% of the 40 million people living with HIV/AIDS are in sub-Saharan Africa,
where AIDS is the leading cause of death. Ethiopia is the second most populous and one of the
seriously affected countries in sub Saharan Africa.
The study focused on a data mining technique to predict future living status of HIV/AIDS
patients at the time of drug regimen change when the patients become toxic to the currently
taking ART drug combination.
The data is taken from University of Gondar Hospital ART program database. Hybrid
methodology is followed to explore the application of data mining on ART program dataset.
Data cleaning, handling missing values and data transformation were used for preprocessing
the data. WEKA 3.7.9 data mining tools, classification algorithms and expertise are utilized as
means to address the research problem. By using four different classification algorithms, (i.e.
J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their
parameters thirty two models were built on the pre-processed University of Gondar ART
program dataset.
The performances of the models were evaluated using the standard metrics of accuracy,
precision, recall and F-measure. The most effective model to predict the status of HIV patients
with drug regimen substitution is pruned J48 decision tree with a classification accuracy of
98.01%.
This study extract interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4
count, Age, Weight and Gender so as to predict the status of drug regimen substitution. The
outcome of this study can be used as an assistant tool for the clinician to help them make more
appropriate drug regimen substitution. Future research directions are forwarded to come up
with an applicable system in the area of the study.
