This paper is in the following e-collection/theme issue:

Coronary Heart Disease (109) Clinical Information and Decision Making (1915) Decision Support for Health Professionals (1539) Machine Learning (2076) Cardiac Risk and Cardiac Risk Calculators (102) Cardiovascular Disease Prevention (206)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68066, first published .
Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

Authors of this article:

Thien Vu1, 2, 3 Author Orcid Image ;   Yoshihiro Kokubo4 Author Orcid Image ;   Mai Inoue1 Author Orcid Image ;   Masaki Yamamoto1 Author Orcid Image ;   Attayeb Mohsen1, 5 Author Orcid Image ;   Agustin Martin-Morales1 Author Orcid Image ;   Research Dawadi1 Author Orcid Image ;   Takao Inoue1, 6 Author Orcid Image ;   Jie Ting Tay1 Author Orcid Image ;   Mari Yoshizaki1 Author Orcid Image ;   Naoki Watanabe1 Author Orcid Image ;   Yuki Kuriya1 Author Orcid Image ;   Chisa Matsumoto4, 7 Author Orcid Image ;   Ahmed Arafa4, 8 Author Orcid Image ;   Yoko M Nakao4 Author Orcid Image ;   Yuka Kato4, 9 Author Orcid Image ;   Masayuki Teramoto4 Author Orcid Image ;   Michihiro Araki1, 10, 11 Author Orcid Image
Article Authors Cited by (8) Tweetations (2) Metrics

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  2. Cheng N, Chen Y, Jin L, Chen L. Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES. BMC Medical Informatics and Decision Making 2025;25(1) View
  3. Dastani M, Ghorbani M, Eskandarioun M, Hassani Goodarzi T, Torabian A, Talebnia S. Association Rule Mining Analysis of Cardiovascular Risk Factors in the CDC Diabetes Health Indicators Dataset. InfoScience Trends 2025;2(6):73 View
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  5. Greco A, Capodanno D. Personalized Treatment of Patients with Coronary Artery Disease: The Value and Limitations of Predictive Models. Journal of Cardiovascular Development and Disease 2025;12(9):344 View
  6. Yang Y, Mei C, Guo X, Chen J, Tao T, Wang Q. Interpretable machine learning for predicting early neurological deterioration in symptomatic intracranial atherosclerotic stenosis. Frontiers in Neurology 2025;16 View
  7. Alabi A, Akinpeloye O, Izinyon O, Amusa T, Famotire A. From Logistic Regression to Foundation Models: Factors Associated With Improved Forecasts. Cureus 2025 View
  8. Jiang H, Li Y. Sex-specific machine learning models for cardiovascular disease risk prediction in adults aged ≥ 80 years: insights from the Chinese longitudinal healthy longevity survey. BMC Geriatrics 2025 View