Application of Machine Learning Algorithms in Predicting Major Adverse Cardiovascular Events after Percutaneous
Min Chen1, Cuiling Sun2,3, Li Yang1,4
1Department of Cardiology, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, 230011 Hefei, Anhui, China.
Insights
A new logistic regression model accurately predicts major adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients. This tool aids clinicians in personalized risk assessment.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Developing predictive models for major adverse cardiovascular events (MACE) is crucial for patients undergoing percutaneous coronary intervention (PCI).
- New-onset ST-segment elevation myocardial infarction (STEMI) patients represent a high-risk population requiring precise risk stratification post-PCI.
- Machine learning (ML) algorithms offer potential for enhancing the accuracy of cardiovascular event prediction.
Purpose of the Study:
- To develop and validate a predictive model for MACE following PCI in new-onset STEMI patients.
- To compare the performance of four distinct ML algorithms in predicting MACE risk.
- To identify key predictors of MACE and visualize risk using a clinical tool.
Main Methods:
- Retrospective data analysis of 250 new-onset STEMI patients undergoing PCI.
- Application of four ML algorithms: K-nearest neighbors (KNN), support vector machine (SVM), Complement Naive Bayes (CNB), and logistic regression.
- Feature selection using Boruta algorithm, model evaluation via AUC, sensitivity, specificity, and risk visualization with a nomogram based on SHAP analysis.
Main Results:
- Logistic regression demonstrated superior performance with an AUC of 0.814 (training) and 0.776 (validation).
- Seven key predictors for MACE were identified: Killip classification, Gensini score, blood urea nitrogen (BUN), heart rate (HR), creatinine (CR), glutamine transferase (GLT), and platelet count (PCT).
- The constructed nomogram provided accurate risk predictions, showing strong agreement between predicted and observed outcomes.
Conclusions:
- The developed logistic regression model is effective for predicting MACE risk in STEMI patients post-PCI.
- The nomogram serves as a valuable and practical tool for clinicians, facilitating personalized risk assessment.
- Improved clinical decision-making and patient management can be achieved through this predictive tool.
Background:
This study aimed to develop and validate a predictive model for major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI) in patients with new-onset ST-segment elevation myocardial infarction (STEMI) using four machine learning (ML) algorithms.
Methods:
Data from 250 new-onset STEMI patients were retrospectively collected. Feature selection was performed using the Boruta algorithm. Four ML algorithms-K-nearest neighbors (KNN), support vector machine (SVM), Complement Naive Bayes (CNB), and logistic regression-were applied to predict MACE risk. Model performance was evaluated using area under the curve (AUC), sensitivity, and specificity. Shapley Additive Explanations (SHAP) analysis was used to rank feature importance, and a nomogram was constructed for risk visualization.
Results:
Logistic regression showed the best performance (AUC = 0.814 in training, 0.776 in validation) compared to KNN, SVM, and CNB. SHAP analysis identified seven key predictors, including Killip classification, Gensini score, blood urea nitrogen (BUN), heart rate (HR), creatinine (CR), glutamine transferase (GLT), and platelet count (PCT). The nomogram provided accurate risk predictions with strong agreement between predicted and observed outcomes.
Conclusions:
The logistic regression model effectively predicts MACE risk after PCI in STEMI patients. The nomogram serves as a practical tool for clinicians, supporting personalized risk assessment and improving clinical decision-making.
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