A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease
Objective: To develop and optimize a machine learning prediction model for cardiovascular events during exercise evaluation in patients with coronary heart disease (CHD). Methods: 16,645 cases of cardiopulmonary exercise testing (CPET) conducted in patients with CHD from January 2016 to September 20...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/20/6061 |
| _version_ | 1797472504727470080 |
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| author | Tao Shen Dan Liu Zi Lin Chuan Ren Wei Zhao Wei Gao |
| author_facet | Tao Shen Dan Liu Zi Lin Chuan Ren Wei Zhao Wei Gao |
| author_sort | Tao Shen |
| collection | DOAJ |
| description | Objective: To develop and optimize a machine learning prediction model for cardiovascular events during exercise evaluation in patients with coronary heart disease (CHD). Methods: 16,645 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. Results: Cardiovascular 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. Conclusions: Machine 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. |
| first_indexed | 2024-03-09T20:02:40Z |
| format | Article |
| id | doaj.art-3202897989f44e1ea107a71c585b7e8c |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T20:02:40Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-3202897989f44e1ea107a71c585b7e8c2023-11-24T00:40:09ZengMDPI AGJournal of Clinical Medicine2077-03832022-10-011120606110.3390/jcm11206061A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart DiseaseTao Shen0Dan Liu1Zi Lin2Chuan Ren3Wei Zhao4Wei Gao5Department of Cardiology, Peking University Third Hospital, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing 100191, ChinaDepartment of Cardiology, Peking University Third Hospital, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing 100191, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaDepartment of Cardiology, Peking University Third Hospital, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing 100191, ChinaDepartment of Cardiology, Peking University Third Hospital, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing 100191, ChinaDepartment of Cardiology, Peking University Third Hospital, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing 100191, ChinaObjective: To develop and optimize a machine learning prediction model for cardiovascular events during exercise evaluation in patients with coronary heart disease (CHD). Methods: 16,645 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. Results: Cardiovascular 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. Conclusions: Machine 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.https://www.mdpi.com/2077-0383/11/20/6061coronary heart diseasecardiopulmonary exercise testingexercise safetyexercise risk precautionmachine learning |
| spellingShingle | Tao Shen Dan Liu Zi Lin Chuan Ren Wei Zhao Wei Gao A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease Journal of Clinical Medicine coronary heart disease cardiopulmonary exercise testing exercise safety exercise risk precaution machine learning |
| title | A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease |
| title_full | A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease |
| title_fullStr | A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease |
| title_full_unstemmed | A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease |
| title_short | A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease |
| title_sort | machine learning model to predict cardiovascular events during exercise evaluation in patients with coronary heart disease |
| topic | coronary heart disease cardiopulmonary exercise testing exercise safety exercise risk precaution machine learning |
| url | https://www.mdpi.com/2077-0383/11/20/6061 |
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