Explainable Artificial Intelligence for Healthcare Applications Using Random Forest Classifier with LIME and SHAP
Mrutyunjaya Panda, Soumya Ranjan Mahanta
Abstract
With advances in computationally efficient artificial intelligence (AI) techniques and their numerous applications in our everyday lives, there is a pressing need to understand the computational details hidden in black-box AI techniques, such as the most popular machine learning and deep learning techniques, through more detailed explanations. The concept of explainable AI (xAI) has emerged from these challenges and has recently garnered more attention from researchers by comprehensively integrating explainability into traditional AI systems. This has led to the development of appropriate frameworks for successful applications of xAI in real-life scenarios, addressing innovations, risk mitigation, ethical issues, and logical values for users. In this book chapter, an in-depth analysis of several xAI frameworks and methods, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), is provided. A Random Forest Classifier, serving as a black-box AI, is applied to a publicly available diabetes symptoms dataset with LIME and SHAP for better interpretations. The results obtained are promising in terms of transparency, validity, and trustworthiness in diabetes disease prediction.