The next frontier of explainable artificial intelligence (XAI) in healthcare services: A study on PIMA diabetes dataset

Abstract

The integration of Artificial Intelligence (AI) in healthcare has revolutionized disease diagnosis and risk prediction. However, the "black-box" nature of AI models raises concerns about trust, interpretability, and regulatory compliance. Explainable AI (XAI) addresses these issues by enhancing transparency in AI-driven decisions. This study explores the role of XAI in diabetes prediction using the PIMA Diabetes Dataset, evaluating machine learning models—logistic regression, decision trees, random forests, and deep learning—alongside SHAP and LIME explainability techniques. Data pre-processing includes handling missing values, feature scaling, and selection. Model performance is assessed through accuracy, AUC-ROC, precision-recall, F1-score, and computational efficiency. Findings reveal that the Random Forest model achieved the highest accuracy (93%) but required post-hoc explainability. Logistic Regression provided inherent interpretability but with lower accuracy (81%). SHAP identified glucose, BMI, and age as key diabetes predictors, offering robust global explanations at a higher computational cost. LIME, with lower computational overhead, provided localized insights but lacked comprehensive interpretability. SHAP’s exponential complexity limits real-time deployment, while LIME’s linear complexity makes it more practical for clinical decision support.These insights underscore the importance of XAI in enhancing transparency and trust in AI-driven healthcare. Integrating explainability techniques can improve clinical decision-making and regulatory compliance. Future research should focus on hybrid XAI models that optimize accuracy, interpretability, and computational efficiency for real-time deployment in healthcare settings.

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