Interpretable Drug Resistance Prediction for Patients on Anti-Retroviral Therapies (ART)

The challenge of eliminating HIV transmission is a critical and complex under taking, particularly in Africa, where countries like Uganda are grappling with a staggering 1.6 million people living with the disease. The virus’s fast pace of mutation is one of the main challenges in this battle, which often leads to the development of drug resistance and makes it difficult to provide effective treatment through AntiRetroviral Therapies (ART). By leveraging the latest innovations in Smart Technologies and Systems, such as Machine Learning, Artificial Intelligence, and Deep Learning, we can create novel approaches to tackle this issue. We presented a model that predicts which HIV patients are likely to develop drug resistance using viral load laboratory test data and machine learning algorithms. On the remaining 30% of the data, we tested our algorithms after painstakingly training and validating them on the previous 70%. Our findings were remarkable: the Decision Tree algorithm outperformed four other comparative algorithms with an f1 scoring mean of 0.9949, greatly improving our ability to identify drug resistance in HIV patients. Our research highlights the potential of combining data from viral load tests with machine learning techniques to identify patients who are likely to develop treatment resistance. These findings are a significant step forward in our ongoing fight against HIV, and we are confident that they will pave the way for new, innovative solutions to address this global health crisis.