ArticleJournal of medical Internet research2024
Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Risk prediction models for peritoneal dialysis-associated peritonitis: a systematic review and meta-analysis.International urology and nephrology · 2026Pooled it
- Exploiting Unsupervised Free-Living Data for Cardiorespiratory Fitness Estimation: Systematic Review and Meta-Analysis.JMIR mHealth and uHealth · 2026Pooled it
- Smart Technology, Fragile Hearts: Navigating AI's Challenges and Limitations in Heart Failure Management.Current heart failure reports · 2026Review
- Sample size calculation for training ensemble machine learning models on health data.Patterns (New York, N.Y.) · 2026Article
- Robust Non-Invasive Cardiac Index Prediction via Feature Integration and Data-Augmented Neural Networks.Bioengineering (Basel, Switzerland) · 2026Article
- A leakage-controlled machine learning framework for postprandial triglyceride phenotyping using synthetic clinical data.Scientific reports · 2026Article
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Literature-informed ensemble machine learning for three-year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model.Diabetes, obesity & metabolism · 2026Article
- An explainable machine learning framework for cardiovascular risk prediction using structured health data.Frontiers in artificial intelligence · 2026Article
- An interpretable machine learning model for predicting 1-year major adverse cardiovascular events in patients with type 2 diabetes and hypertension.Frontiers in medicine · 2026Article
- Development and evaluation of machine learning models for predicting relapse in idiopathic nephrotic syndrome.Frontiers in endocrinology · 2026Article
- Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease.Scientific reports · 2025Article
- Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations.Life (Basel, Switzerland) · 2025Review
- Machine Learning Estimation of Myocardial Ischemia Severity Using Body Surface ECG.Computing in cardiology · 2024Article
- 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 · 2024Article
- Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.