DEVELOPMENT OF PREDICTIVE MODEL FOR SURVIVAL OF PAEDIATRIC HIV/AIDS PATIENTS IN SOUTH WESTERN NIGERIA USING DATA MINING TECHNIQUES

Introduction Disease epidemics are common in developing nations especially in Sub Saharan Africa where Human Immunodeficiency Virus /Acquired Immunodeficiency Syndrome (HIV/AIDS) is the most prevalent of all. HIV/AIDS has a devastating impact on its carriers most especially children (1>age≥15). To promote “wellbeing for all at all ages” which is one of the seventeen sustainable development goal (SDGs) adopted by the general assembly of the United Nations, there is need to pay grave attention to the survival of HIV/AIDS pediatric patients as child’s death is emotionally and physically challenging for the mourning parents. This paper identifies survival variables for HIV/AIDS Pediatric patients who are receiving antiretroviral drugs in Southwestern Nigeria, predictive models were developed and compare in order to select the more suitable one. Methodology Pediatric HIV/AIDS patients’ data (216) were collected from two health institutions, preprocessed and the 10-fold cross validation technique was used to partition the datasets into training and testing data. Predictive models were developed using supervised learning techniques (Naïve Bayes’ and Multi-Layer Perception (MLP)) and the Waikato Environment for Knowledge Analysis (WEKA) was used to simulate the models. CD4 count, Viral Load, Opportunistic infections and Nutritional status were used as the independent variables for the prediction Result Accuracy MAER RMSE RAE ROC Area LOB MLP 99.7% 0.022 0.0962 4.48% 0.992 0.008 Naïve Bayes’ 81.02% 0.2025 0.2920 40.92% 0.993 0.007 Keywords: Naïve Bayes’, MLP, Mean absolute error (MAER), Root mean square error (RME), Root absolute error (RAE), Recall Area (ROC) References (1) Giaquinto, C., Rage, E., Giarcoment, V., Rampson, O., and Elia, D.R (1998). Mother to Child Transmission Current Knowledge and on Going Studies. International Journal of Gynaecology Obstetician, 68: 161-165