An Explainable AI Framework for Lifestyle-Based Healthcare Prediction: A Comprehensive Survey

Abstract: Artificial Intelligence (AI) has become a driving force in preventive healthcare, enabling data-driven prediction and early detection of diseases. However, black-box models often lack interpretability, limiting clinical trust and practical adoption. This survey presents a comprehensive analysis of recent Explainable AI (XAI)-driven healthcare prediction systems focusing on lifestyle-based risk assessment. The study reviews six prominent works integrating machine learning (ML), deep learning, and XAI methods such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) for disease detection—including cardiometabolic conditions, diabetes, heart disease, and mental health risk assessment. Key findings emphasise that explanation consistency, data imbalance, and lack of multimodal integration remain open challenges. This paper consolidates methodologies, compares architectural frameworks, identifies research gaps, and proposes a unified model for lifestyle-based multi-disease prediction using interpretable ML. The proposed architecture integrates SHAP and LIME with Random Forest and Logistic Regression to generate transparent predictions, enhancing clinical usability and preventive intervention design.

Keywords: Explainable Artificial Intelligence, Healthcare Analytics, Lifestyle-Based Prediction, SHAP, LIME, Machine Learning, Preventive Healthcare, Disease Risk Prediction