Korean researchers developed K-CAD, a machine learning model using ridge regression that achieved 76% accuracy in predicting coronary artery disease, significantly outperforming conventional prediction tools (68-71% accuracy) in Asian populations. The model analyzed data from 4,696 Korean patients and was validated in cohorts totaling over 117,000 individuals, demonstrating superior ability to identify patients with significant coronary blockages. This represents a meaningful advance in cardiovascular risk assessment, as existing prediction models were developed primarily from Western populations and perform poorly in Asian patients due to ethnic differences in atherosclerosis patterns and risk factors. The improved accuracy could lead to better screening decisions, potentially reducing unnecessary cardiac testing in low-risk patients while ensuring high-risk individuals receive appropriate evaluation. However, this preprint awaits peer review, and the findings need validation in other Asian populations beyond Korea. The model's clinical implementation would require integration into healthcare systems and comparison with emerging biomarkers. While confirmatory rather than paradigm-shifting, this work addresses a significant gap in personalized cardiovascular medicine for Asian populations.