ArticleJournal of clinical medicine2022
A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.Pharmacology research & perspectives · 2026Article
- Review
- Optimization of artificial intelligence models for prediction of new-onset cardiovascular disease in patients with arterial hypertension.PLOS digital health · 2026Article
- Understanding cardiovascular healthcare professionals' knowledge and attitudes toward exercise-based cardiac rehabilitation in coronary heart disease.Frontiers in cardiovascular medicine · 2026Article
- The relationship between inspiratory muscle strength and exercise tolerance in patients with coronary heart disease.Cardiology journal · 2026Article
- Deep learning prediction of peak oxygen uptake in patients with coronary heart disease: a retrospective study.BMJ open · 2025Article
- Role of immature granulocyte and blood biomarkers in predicting perforated acute appendicitis using machine learning model.World journal of clinical cases · 2025Article
- Machine learning models for reinjury risk prediction using cardiopulmonary exercise testing (CPET) data: optimizing athlete recovery.BioData mining · 2025Article
- A prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning.BMC geriatrics · 2025Article
- Machine learning predicts peak oxygen uptake and peak power output for customizing cardiopulmonary exercise testing using non-exercise features.European journal of applied physiology · 2024Article
- Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.Cell metabolism · 2024Review
- The role of machine learning methods in physiological explorations of endurance trained athletes: a mini-review.Frontiers in sports and active living · 2024Review
- The effects of CPET-guided cardiac rehabilitation on exercise tolerance in older persons with CHD after PCI.Scientific reports · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo develop and optimize a machine learning prediction model for cardiovascular events during exercise evaluation in patients with coronary heart disease (CHD).
methods16,645 cases of cardiopulmonary exercise testing (CPET) conducted in patients with CHD from January 2016 to September 2019 were retrospectively included. Clinical data before testing and data during exercise were collected and analyzed.
resultsCardiovascular events occurred during 505 CPETs (3.0%). No death was reported. Predictive accuracy of the model was evaluated by area under the curve (AUC). AUCs for the SVM, logistic regression, GBDT and XGBoost were 0.686, 0.778, 0.784, and 0.794 respectively.
conclusionsMachine learning methods (especially XGBoost) can effectively predict cardiovascular events during exercise evaluation in CHD patients. Cardiovascular events were associated with age, male, diabetes and duration of diabetes, myocardial infarction history, smoking history, hyperlipidemia history, hypertension history, oxygen uptake, and ventilation efficiency indicators.
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