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

Viewpoints and Perspectives (575) Artificial Intelligence (1773) Clinical Information and Decision Making (1686) Decision Support for Health Professionals (1360) Machine Learning (1769) Cardiovascular Disease Prevention (170)

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 (6) Tweetations Metrics

Journals

  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
  2. Kasartzian D, Tsiampalis T. Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations. Life 2025;15(1):94 View
  3. Bai T, Xu M, Zhang T, Jia X, Wang F, Jiang X, Wei X. Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease. Scientific Reports 2025;15(1) View
  4. Lee J, Shin M. Cross-Database Learning Framework for Electrocardiogram Arrhythmia Classification Using Two-Dimensional Beat-Score-Map Representation. Applied Sciences 2025;15(10):5535 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
  2. Saha D, Guha S, Kundu K, Das S, Chatterjee S, Banik P, Mondal S. 2025 International Conference on Computer, Electrical & Communication Engineering (ICCECE). Heart Disease Prediction Using Logistic Regression View