Publication

Abstract : A comprehensive analysis was performed using the AIDS Clinical Trials Group 175 dataset to improve the accuracy of predicting AIDS disease progression. The primary objective was to integrate machine learning techniques to predict AIDS disease outcomes based on clinical, demographic, and treatmentrelated variables. Several machine learning models, including advanced neural network architectures, were developed and thoroughly evaluated. The study highlighted the ability of machine learning to accurately identify patterns and risk factors associated with AIDS disease progression, thereby improving treatment strategies and patient management. Extensive comparisons of several machine learning models were performed to evaluate their performance and robustness. The aim is to demonstrate the important role of artificial intelligence in predicting and diagnosing AIDS early, thereby contributing to better healthcare outcomes. The comprehensive evaluation of model performance in this study is expected to support future advances in prediction models for AIDS and other chronic diseases.