Analyzing predictors of in-hospital mortality in patients with acute ST-segment elevation myocardial infarction using
Mengge Gong1, Dongjie Liang1, Diyun Xu1
1Department of Cardiovascular Medicine, The Heart Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
Insights
A new AGCOSCA-SVM model accurately predicts ST-segment elevation myocardial infarction (STEMI) using key patient data. This advanced framework offers improved diagnostic accuracy for this severe cardiac condition.
Area of Science:
- Cardiology
- Computational Intelligence
- Machine Learning
Background:
- Acute ST-segment elevation myocardial infarction (STEMI) is a critical condition caused by complete coronary artery blockage.
- Early and accurate prediction of STEMI is vital for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a novel predictive framework for STEMI using evolutionary computation and machine learning.
- To enhance the prediction of STEMI by optimizing feature selection and classification.
Main Methods:
- A hybrid AGCOSCA-SVM model was developed, integrating the AGCOSCA algorithm with Support Vector Machine (SVM).
- The AGCOSCA algorithm, a variant of the Sine Cosine Algorithm, was enhanced with crossover and observation bee strategies.
- The framework was trained and tested on a dataset of 3205 STEMI patients, with AGCOSCA refining feature selection for SVM.
Main Results:
- The AGCOSCA-SVM model achieved high diagnostic performance, with Accuracy (97.83%), Sensitivity (93.75%), and Specificity (96.67%).
- Key predictors identified include acute kidney injury (AKI) stage, fibrinogen, mean platelet volume (MPV), FT3, diuretics, and Killip class.
- AGCOSCA-SVM outperformed traditional machine learning methods in predicting STEMI.
Conclusions:
- The AGCOSCA-SVM framework demonstrates significant potential as a clinical decision support tool for STEMI diagnosis.
- The model's ability to handle complex, nonlinear data relationships makes it suitable for smaller patient datasets.
- This approach offers a promising avenue for improving the early detection and management of STEMI.
Abstract:
Acute ST-segment elevation myocardial infarction (STEMI) is a severe cardiac ailment characterized by the sudden complete blockage of a portion of the coronary artery, leading to the interruption of blood supply to the myocardium. This study examines the medical records of 3205 STEMI patients admitted to the coronary care unit of the First Affiliated Hospital of Wenzhou Medical University from January 2014 to December 2021. In this research, a novel predictive framework for STEMI is proposed, incorporating evolutionary computational methods and machine learning techniques. A variant algorithm, AGCOSCA, is introduced by integrating crossover operation and observation bee strategy into the original Sine Cosine Algorithm (SCA). The effectiveness of AGCOSCA is initially validated using IEEE CEC 2017 benchmark functions, demonstrating its ability to mitigate the deficiency in local mining after SCA random perturbation. Building upon this foundation, the AGCOSCA approach has been paired with Support Vector Machine (SVM) to forge the predictive framework referred to as AGCOSCA-SVM. Specifically, AGCOSCA is employed to refine the selection of predictors from a substantial feature set before SVM is utilized to forecast the occurrence of STEMI. In our analysis, we observed that SVM excels at managing nonlinear data relationships, a strength that becomes particularly prominent in smaller datasets of STEMI patients. To assess the effectiveness of AGCOSCA-SVM, diagnostic experiments were conducted based on the STEMI sample data. Results indicate that AGCOSCA-SVM outperforms traditional machine learning methods, achieving superior Accuracy, Sensitivity, and Specificity values of 97.83 %, 93.75 %, and 96.67 %, respectively. The selected features, such as acute kidney injury (AKI) stage, fibrinogen, mean platelet volume (MPV), free triiodothyronine (FT3), diuretics, and Killip class during hospitalization, are identified as crucial for predicting STEMI. In conclusion, AGCOSCA-SVM emerges as a promising model framework for supporting the diagnostic process of STEMI, showcasing potential applications in clinical settings.
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