EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION AND RISK STRATIFICATION OF CHRONIC DISEASES IN PAKISTAN'S HEALTHCARE SECTOR

Abstract

Chronic diseases such as diabetes mellitus, cardiovascular diseases, and chronic respiratory conditions represent a rapidly growing public health burden in Pakistan, requiring advanced predictive and decision-support solutions for early detection and effective risk stratification. This study developed and evaluated an Explainable Artificial Intelligence (XAI)-based framework integrated with machine learning models to enhance predictive accuracy and interpretability in chronic disease identification. A quantitative, cross-sectional research design was employed using secondary clinical data extracted from healthcare institutions, comprising patient records and clinician feedback. Multiple machine learning models, including Logistic Regression, Random Forest, and XGBoost, were trained and validated using 10-fold cross-validation, while SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were applied to ensure model transparency. The findings revealed that XGBoost outperformed other models with the highest predictive accuracy, AUC-ROC, and overall classification performance. SHAP analysis identified blood glucose level, blood pressure, body mass index (BMI), and age as the most influential predictors of chronic disease risk. Furthermore, clinician evaluation indicated a high level of trust and acceptance of the XAI-based system, emphasizing the importance of interpretability in clinical decision-making. The study confirms that integrating explainable AI with predictive analytics significantly enhances both model performance and clinical usability in healthcare environments. In conclusion, XAI-based machine learning frameworks offer a robust and transparent approach for early detection and risk stratification of chronic diseases, particularly in resource-constrained healthcare systems such as Pakistan. The study contributes to bridging the gap between AI model accuracy and clinical interpretability, supporting the development of trustworthy and deployable healthcare AI systems.