ArticlePloS one2021
A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 93 citations in OpenAlex.
- Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Accuracy of Machine Learning Models for Early Prediction of Major Cardiovascular Events Post Myocardial Infarction: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2025Pooled it
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Automated surgical phase recognition in pediatric laparoscopic fundoplication using deep learning: a retrospective video-based study.Surgical endoscopy · 2026Article
- Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors.iScience · 2026Article
- AI-powered hierarchical classification of ampullary neoplasms: a deep learning approach using white-light and narrow-band imaging.Surgical endoscopy · 2026Article
- 3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.NPJ digital medicine · 2026Article
- Development and validation of predictive models for prognostic assessment in patients with acute non-ST-segment elevation myocardial infarction: a multicentre real-world study from China.BMC medical informatics and decision making · 2026Article
- Artificial intelligence in the cath lab: Bridging predictive models and real-time procedural decision support.EXCLI journal · 2026Review
- Soft-voting classifier for outcome prediction in traumatic brain injury patients with clear consciousness: model development and error pattern analysis.Neurosurgical review · 2025Article
- Reconceptualizing Scoring Reliability Through Linguistic Similarity.Educational and psychological measurement · 2025Article
- Risk Prediction of Major Adverse Cardiovascular Events Within One Year After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome: Machine Learning-Based Time-to-Event Analysis.JMIR medical informatics · 2025Observational
- Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study.JMIR medical informatics · 2025Article
- Machine Learning in Predicting Cardiac Events for ESRD Patients: A Framework for Clinical Decision Support.Diagnostics (Basel, Switzerland) · 2025Article
- Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.Internal medicine (Tokyo, Japan) · 2025Article
- Artificial Intelligence and Its Role in the Diagnosis and Prediction of Adverse Events in Acute Coronary Syndrome: A Narrative Review of the Literature.Life (Basel, Switzerland) · 2025Review
- Integrating convolutional neural networks with ensemble methods for enhanced diabetes diagnosis: a multi-dataset evaluation.Frontiers in medicine · 2025Article
- Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicenter outpatient cohort.Frontiers in medicine · 2025Article
- Machine learning with clinical and intraoperative biosignal data for predicting postoperative delirium after cardiac surgery.iScience · 2024Article
- Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).Scientific reports · 2024Article
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3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSome researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms.
methodsWe used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (30%). Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. Lastly, we compared the performance in the area under the ROC curve (AUC), accuracy, precision, recall, and F-score.
resultsThe accuracies for RF, ET, GBM, and SVE were (88.85%, 88.94%, 87.84%, 90.93%) for complete dataset, (84.81%, 85.00%, 83.70%, 89.07%) STEMI, (88.81%, 88.05%, 91.23%, 91.38%) NSTEMI. The AUC values in RF were (98.96%, 98.15%, 98.81%), ET (99.54%, 99.02%, 99.00%), GBM (98.92%, 99.33%, 99.41%), and SVE (99.61%, 99.49%, 99.42%) for complete dataset, STEMI, and NSTEMI, respectively. Consequently, the accuracy and AUC in SVE outperformed other ML models.
conclusionsThe performance of our SVE was significantly higher than other machine learning models (RF, ET, GBM) and its major prognostic factors were different. This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients.
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