Application of Data Mining Techniques to Predict Antiretroviral Therapy Initation Time the Case of Adama and Ambo Hospitals, Oromia Regional State

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Addis Ababa University


Background: AIDS patients receive antiretroviral treatment (ART) which they need to take every day for the rest of their life. To maintain treatment efficacy, it is necessary to start the treatment at a suitable time. Although the debate regarding when to start antiretroviral therapy has been present for over two decades, consensus on this question has been hard to achieve. This lack of clarity continues in the current era, with major guidelines recommending very different treatment strategies. Objective: The purposes of this research are to assess the applicability of different data mining techniques to predict the initiation time for Antiretroviral Treatment (ART), to identify attributes that are associated with initiation time of ART and to develop a model that can be used to predict the initiation time for Antiretroviral Treatment (ART) using data obtained from Adama and Ambo ART clinic. Method: To undertake this study a hybrid Data mining process model has been employed. The study used 11,440 instances, ten predicting attributes and one outcome variables to run the experiments. Accordingly, Apriori algorithm is used to extract association rules while classification algorithms such as J48 decision tree, PART rule induction and Naïve Bayes were implemented to build predictive models. Result: Experimental result shows that the model developed using AdaBoostM1withpruned PART registers the highest accuracy of 95.62% as compared to Naïve Bayes and J48. The finding of the study clearly presents that Sex, age, OACD4, OAWHO Stage, Family planning and Occupation attributes are best predicts used to predict ART Initiation Time. Conclusion: The study comes up with a predictive model that assists practitioners to predict whether the pre-ART patients should start the treatment "immediately”, “Early” or "Delayed".



Data Mining Techniques