An Adaptive Neuro-Fuzzy Inference System for Predicting the Treatment Response of Human Immunodeficiency Virus Patients

Authors: Aroyehun, A. A., Adebayo, A. A. Ayinla, N.J., and Asubiaro, SC Department of Computer Science, Adeyemi College of Education

The study examined the spread and significance of HIV on the development of health care system. The study simulates the prediction of the spread and treatment response of Human Immunodeficiency Virus (HIV) patient using Adaptive Neuro-fuzzy Inference System (ANFIS) model. ANFIS model is used to predict treatment response of HIV patient and to determine their survival rate. . The developed model, accommodate the baseline Viral Load (VL), CD-4 count, treatment history and time to follow-up as input variables to estimate treatment response accuracy. The study contribute to knowledge with the interaction of (ANFIS) with (HIV) patient’s response rate using Function Approximation (FA) approach to improve the inference technique of prediction and assists the medical staff to improve on accuracy of HIV patient survival rate prediction.. A quantitative comparison the study with Sorenson is a standard, which shows how the approximation error develops over the training epochs.