Since the 1990s, global personalized health management has evolved rapidly, centered in the US with multi-country collaboration, transitioning from systematic care to technology integration (e.g., AI, blockchain). Challenges include inefficient data integration, imprecise population segmentation, and static risk assessment. This study proposes a "theory-method-application" framework: a credit model using reliable/integrity/contribution behaviors enhances data quality and sharing via differentiated services; an unsupervised model combining Gol coefficient & PAM algorithm with t-SNE visualization achieves precise risk stratification (validated via Stanford data with contour/elbow metrics); XGBoost with hyperparameter tuning quantifies biopsychosocial-environmental risk impacts via feature importance and partial dependence; a 4-stage digital platform (data collection, visualization, risk assessment, intervention) supports smart contract alerts and dynamic weight management via rolling optimization. Findings show credit models boost data sustainability, segmentation models exhibit stable stratification, XGBoost outperforms benchmarks, and the platform enables real-time alerts and BMI/cardiovascular risk control. The study advances theory (incentive mechanisms, interpretable AI), practice (resource optimization, proactive care), and future directions (wearables integration, hybrid AI, policy innovation), aligning with AI-driven trends in personalized health management.

Jiazhen Zhu. Research on Individualized Health Risk Identification and Intervention Decision Model Supported by Artificial Intelligence. Socio-Economic Statistics Research (2025), Vol. 6, Issue 2: 109-118. https://doi.org/10.38007/SESR.2025.060211.

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