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Development of a Classification Model for CD4 Count of HIV Patients Using Supervised Machine Learning Algorithms: A Comparative Analysis
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Author(s): Peter Adebayo Idowu (Obafemi Awolowo University, Nigeria)and Jeremiah Ademola Balogun (Obafemi Awolowo University, Nigeria)
Copyright: 2019
Pages: 28
Source title:
Computational Models for Biomedical Reasoning and Problem Solving
Source Author(s)/Editor(s): Chung-Hao Chen (Old Dominion University, USA)and Sen-Ching Samson Cheung (University of Kentucky, USA)
DOI: 10.4018/978-1-5225-7467-5.ch006
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Abstract
This chapter was developed with a view to present a predictive model for the classification of the level of CD4 count of HIV patients receiving ART/HAART treatment in Nigeria. Following the review of literature, the pre-determining factors for determining CD4 count were identified and validated by experts while historical data explaining the relationship between the factors and CD4 count level was collected. The predictive model for CD4 count level was formulated using C4.5 decision trees (DT), support vector machines (SVM), and the multi-layer perceptron (MLP) classifiers based on the identified factors which were formulated using WEKA software and validated. The results showed that decision trees algorithm revealed five (5) important variables, namely age group, white blood cell count, viral load, time of diagnosing HIV, and age of the patient. The MLP had the best performance with a value of 100% followed by the SVM with an accuracy of 91.1%, and both were observed to outperform the DT algorithm used.
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