Interpretable Machine Learning Models for Healthcare Predictions

Abstract

Machine learning has gained prominence in healthcare for its predictive capabilities; however, the lack of interpretability in complex models remains a significant challenge. This paper explores the application of interpretable machine learning models in healthcare to improve prediction accuracy while maintaining transparency and trustworthiness. We discuss the development and evaluation of interpretable models using real healthcare datasets, showcasing their ability to provide not only accurate predictions but also insights into the underlying decision-making process. This research contributes to the critical goal of making AI-driven healthcare systems more accountable and accessible to clinicians and patients.