Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction
Background: Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limited. Methods:...
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| Format: | Article |
| Language: | English |
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IMR Press
2025-01-01
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| Series: | Reviews in Cardiovascular Medicine |
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| Online Access: | https://www.imrpress.com/journal/RCM/26/1/10.31083/RCM26102 |
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| author | Yu-Hang Wang Chang-Ping Li Jing-Xian Wang Zhuang Cui Yu Zhou An-Ran Jing Miao-Miao Liang Yin Liu Jing Gao |
| author_facet | Yu-Hang Wang Chang-Ping Li Jing-Xian Wang Zhuang Cui Yu Zhou An-Ran Jing Miao-Miao Liang Yin Liu Jing Gao |
| author_sort | Yu-Hang Wang |
| collection | DOAJ |
| description | Background: Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limited. Methods: In this observational study, 1111 PMI patients (≤55 years) at Tianjin Chest Hospital from 2017 to 2022 were selected and divided according to their SYNTAX scores into a low-risk group (≤22) and medium–high-risk group (>22). These groups were further randomly assigned to a training or test set in a ratio of 7:3. Lasso–logistic was initially used to screen out target factors. Subsequently, Lasso–logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. After comparing prediction performance, the best model was chosen to build a prediction system for coronary artery severity in PMI patients. Results: Glycosylated hemoglobin (HbA1c), angina, apolipoprotein B (ApoB), total bile acid (TBA), B-type natriuretic peptide (BNP), D-dimer, and fibrinogen (Fg) were associated with the severity of lesions. In the test set, the area under the curve (AUC) of Lasso–logistic, RF, KNN, SVM, and XGBoost were 0.792, 0.775, 0.739, 0.656, and 0.800, respectively. XGBoost showed the best prediction performance according to the AUC, accuracy, F1 score, and Brier score. In addition, we used decision curve analysis (DCA) to assess the clinical validity of the XGBoost prediction model. Finally, an online calculator based on the XGBoost was established to measure the severity of coronary artery lesions in PMI patients Conclusions: In summary, we established a novel and convenient prediction system for the severity of lesions in PMI patients. This system can swiftly identify PMI patients who also have severe coronary artery lesions before the coronary intervention, thus offering valuable guidance for clinical decision-making. |
| format | Article |
| id | doaj-art-ad3f71705fed4e399320db9c856a3c62 |
| institution | Kabale University |
| issn | 1530-6550 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Reviews in Cardiovascular Medicine |
| spelling | doaj-art-ad3f71705fed4e399320db9c856a3c622025-01-25T10:41:20ZengIMR PressReviews in Cardiovascular Medicine1530-65502025-01-012612610210.31083/RCM26102S1530-6550(24)01602-8Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial InfarctionYu-Hang Wang0Chang-Ping Li1Jing-Xian Wang2Zhuang Cui3Yu Zhou4An-Ran Jing5Miao-Miao Liang6Yin Liu7Jing Gao8Thoracic Clinical College, Tianjin Medical University, 300070 Tianjin, ChinaSchool of Public Health, Tianjin Medical University, 300070 Tianjin, ChinaThoracic Clinical College, Tianjin Medical University, 300070 Tianjin, ChinaSchool of Public Health, Tianjin Medical University, 300070 Tianjin, ChinaChest Hospital, Tianjin University, 300072 Tianjin, ChinaThoracic Clinical College, Tianjin Medical University, 300070 Tianjin, ChinaThoracic Clinical College, Tianjin Medical University, 300070 Tianjin, ChinaDepartment of Cardiology, Tianjin Chest Hospital, 300222 Tianjin, ChinaThoracic Clinical College, Tianjin Medical University, 300070 Tianjin, ChinaBackground: Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limited. Methods: In this observational study, 1111 PMI patients (≤55 years) at Tianjin Chest Hospital from 2017 to 2022 were selected and divided according to their SYNTAX scores into a low-risk group (≤22) and medium–high-risk group (>22). These groups were further randomly assigned to a training or test set in a ratio of 7:3. Lasso–logistic was initially used to screen out target factors. Subsequently, Lasso–logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. After comparing prediction performance, the best model was chosen to build a prediction system for coronary artery severity in PMI patients. Results: Glycosylated hemoglobin (HbA1c), angina, apolipoprotein B (ApoB), total bile acid (TBA), B-type natriuretic peptide (BNP), D-dimer, and fibrinogen (Fg) were associated with the severity of lesions. In the test set, the area under the curve (AUC) of Lasso–logistic, RF, KNN, SVM, and XGBoost were 0.792, 0.775, 0.739, 0.656, and 0.800, respectively. XGBoost showed the best prediction performance according to the AUC, accuracy, F1 score, and Brier score. In addition, we used decision curve analysis (DCA) to assess the clinical validity of the XGBoost prediction model. Finally, an online calculator based on the XGBoost was established to measure the severity of coronary artery lesions in PMI patients Conclusions: In summary, we established a novel and convenient prediction system for the severity of lesions in PMI patients. This system can swiftly identify PMI patients who also have severe coronary artery lesions before the coronary intervention, thus offering valuable guidance for clinical decision-making.https://www.imrpress.com/journal/RCM/26/1/10.31083/RCM26102premature myocardial infarctionmachine learningprediction system |
| spellingShingle | Yu-Hang Wang Chang-Ping Li Jing-Xian Wang Zhuang Cui Yu Zhou An-Ran Jing Miao-Miao Liang Yin Liu Jing Gao Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction Reviews in Cardiovascular Medicine premature myocardial infarction machine learning prediction system |
| title | Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction |
| title_full | Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction |
| title_fullStr | Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction |
| title_full_unstemmed | Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction |
| title_short | Advanced Machine Learning to Predict Coronary Artery Disease Severity in Patients with Premature Myocardial Infarction |
| title_sort | advanced machine learning to predict coronary artery disease severity in patients with premature myocardial infarction |
| topic | premature myocardial infarction machine learning prediction system |
| url | https://www.imrpress.com/journal/RCM/26/1/10.31083/RCM26102 |
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