Statistical Approach to Enhancing Performance of Logistic Regression Model: Application to HIV/AIDS Data

This paper considered a robust method for modeling and predicting HIV/AIDS status of patients using logistic regression model enhanced with principal component analysis (PCA) and K-medians. In particular, the study developed a computational method for disease classification; and then identified key haematological predictors of HIV/AIDS status. Based on quantitative research design, the utility of the methods is exemplified using real HIV/AIDS data obtained from a polyclinic in the Greater Accra region of Ghana. The data consists of one hundred and fifty (150) patients, eighty (80) of whom are known to have tested positive for HIV/AIDS. The study findings revealed that enhancement in predictive accuracy for a logistic regression is possible by means of incorporating PCA and K-Medians with robust centers. Model 5 was found to be the best predictor of HIV/AIDS status of a patient. It is an integration of both robust principal component analysis and K-Medians clustering into a binary logistic regression model. Its predictive accuracy is over 93%, and with 98% probability per the ROC criterion. The study thus recommends the incorporation of both RPCA and K-Medians with robust centers into binary logistic regression model to enhance its predictive performance.