• Admin .
• 01 Jan, 2025

   :    https://link.springer.com/chapter/10.1007/978-3-031-69197-3_31

Through automated decision-making, Artificial Intelligence (AI) refers to the use of computers to do tasks that often need human intelligence. A sub-field of AI known as Machine Learning (ML) uses techniques to let computers learn without being expressly taught. Unfortunately, users find it challenging to comprehend how ML models generate predictions due to their intricate parameter sets. In this work, ML models that predicted HIV positive patients with viral rebound using clinical, demographic, laboratory, and healthcare facility data were developed. LIME (Local Interpretable Model-agnostic Explanations), which explained specific predictions provided by ML models was focused on. Ensemble learning techniques such as boosting, bagging, stacking and hard voting were applied to reduce errors, improve performance, and boost the overall robustness of predictions. While individual ML models may perform poorly due to their high variation or bias, their integration strengthens each other’s shortcomings and yields more precise predictions. Furthermore, the viral rebound feature interaction for patients was highlighted, which will help health workers in model debugging, building trust and validating predictions.