Machine Learning-Based Predictive Models for Early Detection of Cardiovascular Diseases: A Study Utilizing Patient...

A machine learning model was developed for cardiovascular diseases prediction based on 21,118 patient checkups data from a tertiary medical institution in Seoul, Korea, collected between 2009 and 2021. XGBoost algorithm showed the highest predictive performance, with an average AUROC of 0.877. In survival analysis, XGBSE achieved an AUROC exceeding 0.9 for 2-9 year predictions, with a C-index of 0.878 across all diseases, outperforming Cox regression (C-index of 0.887). A high-performance prediction model for cardiovascular diseases using the XGBSE algorithm was successfully developed and is poised for real-world clinical application following external simplification and validation.