Key result
An XGBoost machine learning model using statewide electronic health records achieved strong discrimination for predicting 1-year incident coronary heart disease, with an AUC of 0.888.
Why the study?
Coronary heart disease progresses over years before serious cardiac events, making early prediction and intervention critical to reducing morbidity, mortality, and healthcare burden.
Does an XGBoost-based machine learning model using electronic health records accurately predict 1-year risk of incident coronary heart disease in the general population?
Cohort (n=2,082,282)
Yes
Does an XGBoost-based machine learning model using electronic health records accurately predict 1-year risk of incident coronary heart disease in the general population?
Effect estimate: AUC 0.888 (95% CI 0.885-0.890)
A machine learning model using statewide electronic health records accurately predicted 1-year risk of incident coronary heart disease, enabling population-scale risk stratification.
May support targeted CHD prevention via EHR tools; leaves open external validation and outcome impact before adoption.
BACKGROUND: Coronary heart disease (CHD), the most common form of heart disease, progresses over years before culminating in serious cardiac events. Early prediction and intervention are critical to reducing CHD-related morbidity, mortality, and healthcare burden. OBJECTIVE: To develop and validate a machine learning model using statewide electronic health records (EHRs) to predict 1-year risk of CHD in the general population of Maine, enabling targeted preventive strategies. METHODS: Two population-based cohorts were constructed from the Maine Health Information Exchange (HIE): a retrospective cohort for model training and calibration (2015–2017, N = 1,042,124), and a prospective cohort for external validation (2016–2018, N = 1,040,158). EHR features included demographics, diagnoses, procedures, medications, labs, and utilization metrics. A multistage modeling pipeline—comprising statistical filtering, XGBoost-based feature selection, risk prediction, and isotonic regression calibration—was used to construct the final model. Validation included discrimination, calibration, and survival analysis. RESULTS: The final XGBoost model achieved strong discrimination: AUC = 0.952 (95% CI: 0.950–0.954) in the retrospective cohort and 0.888 (95% CI: 0.885–0.890) in the prospective cohort. Based on calibrated risk probabilities, the population was stratified into five risk categories: very low (92.30%, N = 960,021), low (6.79%, N = 70,676), medium (0.85%, N = 8,888), high (0.05%, N = 554), and very high (0.002%, N = 19). Among the very high-risk group, 11 individuals (57.89%) developed CHD within one year. CONCLUSIONS: This statewide, HIE-based CHD risk prediction model demonstrates robust performance and real-world applicability. It enables early identification of high-risk individuals and supports population-scale precision prevention through evidence-informed, proactive care.
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Zhang et al. (2025) conducted a cohort in Coronary heart disease (n=2,082,282). XGBoost machine learning risk prediction model was evaluated on Discrimination (AUC) for 1-year incident coronary heart disease in the prospective validation cohort (AUC 0.888, 95% CI 0.885-0.890). An XGBoost machine learning model using statewide electronic health records achieved strong discrimination for predicting 1-year incident coronary heart disease, with an AUC of 0.888.
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