Diagnosis of Acute Coronary Syndrome with a Support Vector Machine
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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.
KeywordsAcute coronary syndrome Artificial intelligence Support vector machine Machine learning
Compliance with Ethical Standards
Conflicts of Interest
This study has no conflict of interest.
- 3.Kurz, M. C., Mattu, A., and Brady, W. J., Acute Coronary Syndrome. Rosen’s Emergency Medicine Chapter 78, 997-1033.E5, Elsevier, 2014.Google Scholar
- 5.Roffi, Marco, Et Al. (2015) Esc guidelines for the management of acute coronary syndromes in patients presenting without persistent st-segment elevation. European Heart J. Ehv320.Google Scholar
- 7.Amsterdam, E. A., Wenger, N. K., Brindis, R. G., Casey, D. E., Ganiats, T. G., Holmes, D. R., and Levine, G. N., 2014 Aha/Acc Guideline For The Management Of Patients With Non–St-Elevation Acute Coronary Syndromes: A Report Of The American College Of Cardiology/American Heart Association Task Force On Practice Guidelines. Journal Of The American College Of Cardiology 64(24):E139–E228, 2014.CrossRefPubMedGoogle Scholar
- 9.Cruz, J. A., and Wishart, D. S., Applications Of Machine Learning İn Cancer Prediction And Prognosis. Cancer Informat. 2:59, 2006.Google Scholar
- 15.Aj, S., and Schölkopf, B., A Tutorial On Support Vector Regression. Neurocolt Technical Report 14(3):199–222, 2004.Google Scholar
- 19.Abe, S., (2005). Support vector machines for pattern classification, 2nd Edn, SpringerGoogle Scholar
- 20.Ha, S. H., and Joo, S. H., A Hybrid Data Mining Method For The Medical Classification Of Chest Pain. International Journal Of Computer And Information Engineering 4(1):33–38, 2010.Google Scholar
- 21.Ghumbre, S., Patil, C., & Ghatol, A. (2011). Heart disease diagnosis using support vector machine. In International Conference On Computer Science And İnformation Technology (Iccsıt’) PattayaGoogle Scholar
- 22.Vadicherla, D., and Sonawane, S., Classification Of Heart Disease Using Svm And Ann. Ijrcct 2(9):693–701, 2013.Google Scholar