ArticleJournal of medical systems2016
Diagnosis of Acute Coronary Syndrome with a Support Vector Machine.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 5 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 5 syntheses or guidelines pooled it, 114 citations in OpenAlex.
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.Computational and mathematical methods in medicine · 2022Pooled it
- Pooled it
- Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review.PloS one · 2021Pooled it
- A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock.Molecular medicine reports · 2020Pooled it
- Artificial Intelligence in Cardiovascular Risk Prediction: An Up-to-Date Narrative Review on the Emerging Role of Lipid Profile-Based Models.Journal of clinical medicine · 2026Review
- Machine learning-based study of breath sounds in children with asthma in remission and cough variant asthma.BMC medical informatics and decision making · 2026Observational
- Adversarial Debiasing for Equitable and Fair Detection of Acute Coronary Syndrome Using 12-Lead ECG.IEEE transactions on bio-medical engineering · 2026Article
- Integration of tongue image features and tongue coating microbiome for differentiating dampness patterns in MASLD.Frontiers in endocrinology · 2026Article
- Metabolic Syndrome Detection Based on Classification of Electrocardiography Signals.Sensors (Basel, Switzerland) · 2025Article
- Clinical study of intelligent tongue diagnosis and oral microbiome for classifying TCM syndromes in MASLD.Chinese medicine · 2025Article
- Improving ACS prediction in T2DM patients by addressing false records in electronic medical records using propensity score.Scientific reports · 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
- Machine Learning Methods for Predicting Syncope Severity in the Emergency Department: A Retrospective Analysis.Health science reports · 2025Article
- Vascular medicine in the 21World journal of clinical cases · 2024Review
- International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction.European heart journal. Digital health · 2024Article
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
- Artificial Intelligence Technologies in Cardiology.Journal of cardiovascular development and disease · 2023Review
- Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact.Research square · 2023Article
- A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium's metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis. However in emergency departments, there are some difficulties for physicians to decide whether hospitalizing, following up or discharging the patient. The aim of the study is to diagnose ACS and helping the physician with his decisionto discharge or to hospitalizevia machine learning techniques such as support vector machine (SVM) by using patient data including age, sex, risk factors, and cardiac enzymes (CK-MB, Troponin I) of patients presenting to emergency department with chest pain. Clinical, laboratory, and imaging data of 228 patients presenting to emergency department with chest pain were reviewedand the performance of support vector machine. Four different methods (Support vector machine (SVM), Artificial neural network (ANN), Naïve Bayes and Logistic Regression) were tested and the results of SVM which has the highest accuracy is reported. Among 228 patients aged 19 to 91 years who were included in the study, 99 (43.4 %) were qualified as ACS, while 129 (56.5 %) had no ACS. The classification model using SVM attained a 99.13 % classification success. The present study showed a 99.13 % classification success for ACS diagnosis attained by Support Vector Machine. This study showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.