ArticleFrontiers in cardiovascular medicine2024
A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
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3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Machine Learning Models in Major Cardiovascular Adverse Events Prediction Based on Coronary Computed Tomography Angiography: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Applications and challenges of biomarker-based predictive models in proactive health management.Frontiers in public health · 2025Pooled it
- The application of coronary CT angiography combined with dynamic electrocardiogram in the diagnosis of myocardial ischemia in coronary heart disease.Frontiers in cardiovascular medicine · 2026Article
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14 authors.
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Abstract
Introduction: Accurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation. Materials and methods: We proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups. Results: By comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729. Conclusion: In sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy.
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