Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP

TL;DR

This paper explores the use of explainable AI techniques, specifically LIME and SHAP, applied to a Random Forest classifier for diabetes prediction, enhancing transparency and trustworthiness in healthcare applications.

Contribution

It demonstrates the application of LIME and SHAP to interpret a Random Forest model in diabetes prediction, emphasizing improved explainability in healthcare AI.

Findings

LIME and SHAP provide clear explanations of model predictions.

Enhanced transparency increases trust in AI-based diabetes diagnosis.

The approach improves understanding of feature importance in healthcare models.

Abstract

With the advances in computationally efficient artificial Intelligence (AI) techniques and their numerous applications in our everyday life, there is a pressing need to understand the computational details hidden in black box AI techniques such as most popular machine learning and deep learning techniques; through more detailed explanations. The origin of explainable AI (xAI) is coined from these challenges and recently gained more attention by the researchers by adding explainability comprehensively in traditional AI systems. This leads to develop an appropriate framework for successful applications of xAI in real life scenarios with respect to innovations, risk mitigation, ethical issues and logical values to the 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) are provided. Random Forest Classifier as black box AI is used on a publicly available Diabetes symptoms dataset with LIME and SHAP for better interpretations. The results obtained are interesting in terms of transparency, valid and trustworthiness in diabetes disease prediction.