Sex-specific machine learning models for cardiovascular disease risk prediction in adults aged ≥ 80 years: insights from the Chinese longitudinal healthy longevity survey

This study developed interpretable, gender-specific machine learning models for CVD risk prediction in the oldest old population using routine biomarkers. The models demonstrated good discrimination and calibration, offering a practical tool for identifying high-risk individuals who may benefit from targeted preventive interventions. These findings suggest the potential utility of biomarker-based machine learning approaches in cardiovascular risk assessment for the rapidly growing elderly population.