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Viewpoints and Perspectives (637) Clinical Information and Decision Making (2095) Decision Support for Health Professionals (1655) Machine Learning (2257) Cardiovascular Disease Prevention (217) Artificial Intelligence (2387)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47645, first published .
Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions

Authors of this article:

Yu-Qing Cai1 Author Orcid Image ;   Da-Xin Gong2 Author Orcid Image ;   Li-Ying Tang1 Author Orcid Image ;   Yue Cai1 Author Orcid Image ;   Hui-Jun Li3 Author Orcid Image ;   Tian-Ci Jing2 Author Orcid Image ;   Mengchun Gong4 Author Orcid Image ;   Wei Hu5 Author Orcid Image ;   Zhen-Wei Zhang6 Author Orcid Image ;   Xingang Zhang7 Author Orcid Image ;   Guang-Wei Zhang2 Author Orcid Image
Article Authors Cited by (29) Tweetations (1) Metrics

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  1. Hwang S, Lee H, Lee J, Lee M, Koyanagi A, Smith L, Rhee S, Yon D, Lee J. Machine Learning–Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan. Journal of Medical Internet Research 2024;26:e52794 View
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  11. LAREYRE F, WEAVER L, AFIFI R, KIANG S, RAFFORT J. Future perspectives of artificial intelligence in vascular surgery. The Journal of Cardiovascular Surgery 2025;66(6) View
  12. Dosis A, Syversen A, Kowal M, Grant D, Tiernan J, Wong D, Jayne D. Exploiting Unsupervised Free-Living Data for Cardiorespiratory Fitness Estimation: Systematic Review and Meta-Analysis. JMIR mHealth and uHealth 2026;14:e69996 View
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  15. Rios-Garcia W, Broncano-Rivera K, MariaFe-Martinez-Acuna , Via-y-Rada-Torres A, Quintana-García L, Mendoza Marcilla J, Saldaña Mercado C, Rios-Garcia A. Prediction of Early-onset Preeclampsia Using Deep Learning: A Scoping Review of Clinical and Imaging Models. Pregnancy Hypertension 2026;44:101462 View
  16. Maciej Tomasz Wieczorek , Jeremi Leon Jasiński , Karolina Julia Hak , Alicja Maria Mitan , Aleksandra Maria Tomaszewska , Weronika Napierała , Kamila Teresa Kańska , Karolina Magda Leszczyńska , Anna Krzysztofik , Karolina Krawczyk . ARTIFICIAL INTELLIGENCE IN CARDIOVASCULAR DISEASE PREDICTION: A COMPREHENSIVE INTEGRATIVE FRAMEWORK FROM MULTIMODAL METHODOLOGIES TO CLINICAL IMPLEMENTATION. International Journal of Innovative Technologies in Social Science 2026;3(1(49)) View
  17. Rao J, Ma X, Li X. SAS-SemiUNet++: A Stochastic Consistency Regularized Framework with Scale-Aware Semantic Recalibration for Cardiac MRI Segmentation. Applied Sciences 2026;16(7):3507 View
  18. Piyavechvirat N, Huang Y, Haq Q. A leakage-controlled machine learning framework for postprandial triglyceride phenotyping using synthetic clinical data. Scientific Reports 2026 View
  19. Zhao M, Li P, Chen F, Wu Z. Development and evaluation of machine learning models for predicting relapse in idiopathic nephrotic syndrome. Frontiers in Endocrinology 2026;17 View
  20. Parise G, Ceravolo R, Lucà F, Gulizia M, Tetta C, Parise O, Nardi F, Grimaldi M, Gelsomino S. Synthetic artificial intelligence in cardiology: from generative models to clinical applications. European Heart Journal Open 2026;6(2) View

Books/Policy Documents

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  2. Vijaya S, Padmapriya D. Artificial Intelligence Based Smart and Secured Applications. View

Conference Proceedings

  1. Sharma J, Thakur P, Singh K, Gupta N. 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA). A Comprehensive Analysis & Performance Evaluation of Machine Learning Techniques in Heart Disease Diagnosis View
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