Optimization Pathways of Health Risk Assessment Models in Clinical Settings: From Theoretical Framework to Practical Applications

Health risk assessment models are vital in clinical decision-making for predicting disease risks and personalizing interventions, yet traditional approaches face challenges in accuracy, interpretability, and adaptability amid growing data complexity. This study explores optimization strategies by systematically examining theoretical frameworks (statistical, machine learning, and hybrid models), evaluation metrics, and practical applications. Proposed improvements focus on data preprocessing, model selection/validation, interpretability enhancement, and clinical integration. Case studies in cardiovascular disease, cancer, and chronic disease management demonstrate their clinical utility, while addressing challenges like data privacy, model updates, and clinical adoption. The findings provide actionable insights to advance precision health management and personalized medicine.