Explainable AI-Driven Health Scoring Framework for Smart City Sustainability

Abstract

The rapid evolution of smart cities demands a transition from reactive healthcare systems to proactive, data-driven health management paradigms that support long-term urban sustainability. Predicting population health status based on lifestyle-related behavioral and physiological factors is critical for enabling early intervention, personalized healthcare, and efficient resource allocation directly contributing to the United Nations Sustainable Development Goals (SDG 3: Good Health and Well-being; SDG 11: Sustainable Cities and Communities). This study proposes an IoT-enabled Explainable Artificial Intelligence (XAI) framework for predictive health scoring as part of sustainable population health management, integrating real-time data acquisition, cloud-based analytics, and interpretable machine learning. To address the limitations of conventional ensemble models particularly the black-box nature and hyperparameter sensitivity of Extreme Gradient Boosting (XGBoost) a Bayesian optimization strategy is employed to automatically fine-tune model parameters, thereby enhancing predictive accuracy and generalization performance. Furthermore, Shapley Additive Explanations (SHAP) are incorporated to provide transparent, interpretable insights into model predictions by quantifying the contribution of individual lifestyle features. Using a publicly available Kaggle dataset (“Health and Lifestyle Data for Regression”), experimental evaluation demonstrates that the proposed Bayesian-Optimized XGBoost model achieves superior performance (Test R2 = 0.878, RMSE = 4.983), outperforming ten benchmark models, including standard XGBoost, which exhibits signs of overfitting (Test R2 = 0.832). The results further reveal that Body Mass Index (BMI) and diet quality are the most influential factors affecting health scores, providing actionable insights for urban health policymakers. The proposed framework highlights the synergy between IoT, optimization techniques, and explainable AI to develop transparent, reliable, and scalable predictive health systems. This work provides a practical foundation for next-generation smart healthcare applications and decision-support systems, advancing the vision of sustainable, data-driven, and human-centric smart cities.