The Prediction of Paediatric HIV/AIDS Patient Survival: A Data Mining Approach
Abstract
This research requires the development of predictive model for determining the survival of Paediatric HIV/AIDS patients who are receiving antiretroviral drugs in the South-western Nigeria. The WEKA software was used in developing the predictive model using Naïve Bayes’ Classifier. Naïve Bayes’ Classifier was used to predict the length of survival of HIV/AIDS patients based on variables like CD4 count, viral load, opportunistic infection and nutritional status. The result shows that Naïve Bayes’ Classification can predict the survival of paediatrics HIV/AIDS patient with an accuracy of 60% to 100% based on selected dependent variables.
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Sam Mateo (2012):
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