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Unravelling the Enigma of Machine Learning Model Interpretability in Enhancing Disease Prediction
Abstract
Machine learning (ML) models have made significant strides in disease prediction, providing new avenues for early detection and intervention. These models have demonstrated remarkable capabilities in analysing vast and complex datasets to identify patterns and trends that can aid in early diagnosis and treatment. However, opacity of these models often leaves healthcare practitioners and patients in the dark about the reasoning behind their predictions, raising concerns about trust, fairness, and practical adoption of AI-based disease prediction. This review delves into the critical topic of interpretability in ML models for disease prediction, its importance, techniques to achieve it, impact on clinical decision-making, challenges, and implications in healthcare. Urgent issues and moral dilemmas pertaining to model interpretability in healthcare, areas for further research to enhance interpretability of predictive models, and applications are also highlighted. Thus, the chapter provides insights into the applicability of AI-driven models to improve healthcare decision-making and patient outcomes.
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