Can machine learning models using routine health examination data effectively predict CAC-defined cardiovascular risk in a Taiwanese population?
Machine learning models using routine health data can serve as scalable pre-screening tools for CAC-defined cardiovascular risk, especially in resource-limited settings.
ML models incorporating routine health examination variables can effectively predict CAC-defined cardiovascular risk and may serve as practical, scalable pre-screening tools within preventive healthcare workflows, particularly in settings where laboratory testing or advanced imaging resources may be limited.
Chuang et al. (Sun,) studied this question.