Can machine learning models using routine health examination data effectively predict CAC-defined cardiovascular risk in a Taiwanese population?
Taiwanese population undergoing routine health examinations
Machine learning models incorporating routine health examination variables
Prediction of CAC-defined cardiovascular risksurrogate
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.
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Shan-Shan Chuang
Kuo-Jang Kao
Shih-Wei Lin
Journal of the Formosan Medical Association
Chang Gung University
Fu Jen Catholic University
Keelung Chang Gung Memorial Hospital
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Chuang et al. (Sun,) studied this question.
www.synapsesocial.com/papers/69a760bfc6e9836116a2dccf — DOI: https://doi.org/10.1016/j.jfma.2026.01.061