A Machine Learning Approach to Aids Clinical Trials Group (Actg) Study

AIDS - Acquired Immunodeficiency Syndrome, is a major health concern, with an estimated 39 million people living with HIV worldwide. [1] Predicting the risk of mortality in AIDS patients is important for optimizing treatment strategies and improving the outcomes. The choice of Antiretroviral Therapy (ART), whether monotherapy or combined therapy, plays a crucial role in optimizing the treatment strategies. This study aims to apply machine learning techniques to predict patient mortality within a certain window of time using the AIDS Clinical Trials Group (ACTG) Study 175 Dataset. The results demonstrate the role of Data Science and the potential of machine learning models to forecast mortality, providing valuable insights for improving the treatment.