ArticleSensors (Basel, Switzerland)2023
A Machine Learning-Based Applied Prediction Model for Identification of Acute Coronary Syndrome (ACS) Outcomes and Mortality in Patients during the Hospital Stay.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review).Sovremennye tekhnologii v meditsine · 2024Pooled it
- Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.BMC medical informatics and decision making · 2026Article
- A Novel Risk Score Based on Lipid-Related Biomarkers for Acute Coronary Syndromes: A Multicenter Machine Learning Study.Reviews in cardiovascular medicine · 2026Article
- Construction and clinical validation of a machine learning-based risk prediction model for poor wound healing after anorectal surgery.Frontiers in surgery · 2026Article
- Comparing Machine Learning Models for Predicting Mortality after Myocardial Infarction: A Systematic Review and Meta-analysis.Archives of academic emergency medicine · 2026Review
- Machine Learning Approach on Predictive Model Establishment for In-Hospital Mortality in Acute Myocardial Infarction Patients Post-Percutaneous Coronary Intervention: Solutions for Databases With Dimensionality Reduction and Class Imbalance.Reviews in cardiovascular medicine · 2025Article
- Survival Prediction in Stomach Cancer with Deep Learning: Unveiling Model Decisions with LIME and SHAP.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Predictive Modeling for Pandemic Forecasting: A COVID-19 Study in New Zealand and Partner Countries.International journal of environmental research and public health · 2025Article
- Profiling of Cardiogenic Shock: Incorporating Machine Learning Into Bedside Management.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Risk factors for heart failure within one year after percutaneous coronary intervention in patients with acute coronary syndrome: development of a predictive model.American journal of translational research · 2025Article
- QTc interval prolongation impact on in-hospital mortality in acute coronary syndromes patients using artificial intelligence and machine learning.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2024Article
- Explainable machine learning models for early gastric cancer diagnosis.Scientific reports · 2024Article
- Development and validation of a prognostic model for predicting post-discharge mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI).Journal of cardiothoracic surgery · 2024Article
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
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3 authors.
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Abstract
Nowadays, machine learning (ML) is a revolutionary and cutting-edge technology widely used in the medical domain and health informatics in the diagnosis and prognosis of cardiovascular diseases especially. Therefore, we propose a ML-based soft-voting ensemble classifier (SVEC) for the predictive modeling of acute coronary syndrome (ACS) outcomes such as STEMI and NSTEMI, discharge reasons for the patients admitted in the hospitals, and death types for the affected patients during the hospital stay. We used the Korea Acute Myocardial Infarction Registry (KAMIR-NIH) dataset, which has 13,104 patients' data containing 551 features. After data extraction and preprocessing, we used the 125 useful features and applied the SMOTETomek hybrid sampling technique to oversample the data imbalance of minority classes. Our proposed SVEC applied three ML algorithms, such as random forest, extra tree, and the gradient-boosting machine for predictive modeling of our target variables, and compared with the performances of all base classifiers. The experiments showed that the SVEC outperformed other ML-based predictive models in accuracy (99.0733%), precision (99.0742%), recall (99.0734%), F1-score (99.9719%), and the area under the ROC curve (AUC) (99.9702%). Overall, the performance of the SVEC was better than other applied models, but the AUC was slightly lower than the extra tree classifier for the predictive modeling of ACS outcomes. The proposed predictive model outperformed other ML-based models; hence it can be used practically in hospitals for the diagnosis and prediction of heart problems so that timely detection of proper treatments can be chosen, and the occurrence of disease predicted more accurately.
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