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Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial
Zhixun Bai1,2,3, Jing Lu4, Ting Li3
1Program of Artificial Intelligence in Medicine, College of Medicine, Soochow University, Suzhou 215123, China.
Insights
Machine learning models, particularly CatBoost, can accurately predict 1-year mortality in ST-segment elevation myocardial infarction (STEMI) patients with hyperuricemia. This offers a practical tool for risk assessment in clinical practice.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Accurate prognosis prediction for high-risk patients is crucial in clinical settings.
- No established methods exist for predicting or monitoring outcomes in ST-segment elevation myocardial infarction (STEMI) patients with hyperuricemia.
- Hyperuricemia is a common comorbidity in STEMI patients, potentially impacting prognosis.
Purpose of the Study:
- To evaluate the performance of various machine learning models in predicting 1-year mortality in STEMI patients with hyperuricemia.
- To compare the predictive accuracy of machine learning models against the traditional GRACE risk score.
- To identify the optimal machine learning model for risk stratification in this patient cohort.
Main Methods:
- A cohort of 656 STEMI patients with hyperuricemia, aged over 18, were enrolled between 2016 and 2020.
- Five machine learning models (logistic regression, k-nearest neighbor, CatBoost, random forest, XGBoost) were compared with the GRACE risk score.
- Model performance was assessed using metrics including accuracy, Area Under the Curve (AUC), precision, and F1 score, with optimization via hybrid sampling.
Main Results:
- The Area Under the Curve (AUC) for the evaluated models ranged from 0.75 to 0.88.
- The CatBoost model demonstrated superior predictive performance with an AUC of 0.87, accuracy of 0.89, precision of 0.84, and F1 score of 0.44 prior to optimization.
- Following hybrid sampling optimization, the CatBoost model achieved significantly higher metrics: 0.96 accuracy, 0.99 AUC, 0.95 precision, and 0.97 F1 score.
Conclusions:
- Machine learning algorithms, especially the CatBoost model, offer accurate prediction of 1-year mortality in STEMI patients with hyperuricemia.
- The optimized CatBoost model provides a highly effective tool for risk stratification and prognosis monitoring in this patient group.
- These findings support the integration of advanced machine learning techniques into clinical practice for improved patient management.
Abstract:
Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, k-nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged >18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, 62.5 ± 13.6 years; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up.
