Diagnosis of Acute Coronary Syndrome with a Support Vector Machine

  • Göksu Bozdereli Berikol
  • Oktay Yildiz
  • İ. Türkay Özcan
Transactional Processing Systems
Part of the following topical collections:
  1. Transactional Processing Systems

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.

Keywords

Acute coronary syndrome Artificial intelligence Support vector machine Machine learning 

References

  1. 1.
    Nichols, M., Townsend, N., Scarborough, P., and Rayner, M., Cardiovascular Disease İn Europe: Epidemiological Update. Eur. Heart J. 34(39):3028–3034, 2013.CrossRefPubMedGoogle Scholar
  2. 2.
    Chen, S. Y., Crivera, C., Stokes, M., Boulanger, L., and Schein, J., Clinical And Economic Outcomes Among Hospitalized Patients With Acute Coronary Syndrome: An Analysis Of A National Representative Medicare Population. Clinicoeconomics And Outcomes Research: Ceor 5:181, 2013.CrossRefGoogle Scholar
  3. 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
  4. 4.
    Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., and Huffman, M. D., Heart Disease And Stroke Statistics-2015 Update: A Report From The American Heart Association. Circulation 131(4):E29, 2015.CrossRefPubMedGoogle Scholar
  5. 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
  6. 6.
    Roger, V., et al., Aha Statistical Update. Heart Disease And Stroke Statistics—2011 Update. A Report From The American Heart Association. Circulation 123:18–209, 2011.CrossRefGoogle Scholar
  7. 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
  8. 8.
    Goldman, L., Cook, E. F., Brand, D. A., Lee, T. H., Rouan, G. W., Weisberg, M. C., and Gottlieb, L., A Computer Protocol To Predict Myocardial İnfarction İn Emergency Department Patients With Chest Pain. New England Journal Of Medicine 318(13):797–803, 1988.CrossRefPubMedGoogle Scholar
  9. 9.
    Cruz, J. A., and Wishart, D. S., Applications Of Machine Learning İn Cancer Prediction And Prognosis. Cancer Informat. 2:59, 2006.Google Scholar
  10. 10.
    Scırıca, B. M., Acute Coronary Syndromeemerging Tools For Diagnosis And Risk Assessment. Journal Of The American College Of Cardiology 55(14):1403–1415, 2010.CrossRefPubMedGoogle Scholar
  11. 11.
    Hsieh, S. L., Hsieh, S. H., Cheng, P. H., Chen, C. H., Hsu, K. P., Lee, I. S., et al., Design Ensemble Machine Learning Model For Breast Cancer Diagnosis. J. Med. Syst. 36:2841–2847, 2012.CrossRefPubMedGoogle Scholar
  12. 12.
    Martis, R. J., Krishnan, M. M. R., Chakraborty, C., Pal, S., Sarkar, D., et al., Automated Screening Of Arrhythmia Using Wavelet Based Machine Learning Techniques. J. Med. Syst. 36:677–688, 2012.CrossRefPubMedGoogle Scholar
  13. 13.
    Xie, J., and Wangc, C., Using Support Vector Machines With A Novel Hybrid Feature Selection Method For Diagnosis Of Erythemato-Squamous Diseases. Expert Systems With Applications 38:5809–5815, 2011.CrossRefGoogle Scholar
  14. 14.
    Ohmann, C., Moustakis, V., Yang, Q., and Lang, K., Evaluation Of Automatic Knowledge Acquisition Techniques İn The Diagnosis Of Acute Abdominal Pain. Artificial Intelligence İn Medicine 8:23–36, 1996.CrossRefPubMedGoogle Scholar
  15. 15.
    Aj, S., and Schölkopf, B., A Tutorial On Support Vector Regression. Neurocolt Technical Report 14(3):199–222, 2004.Google Scholar
  16. 16.
    Akay, D., and Toksarı, M. D., Ant Colony Optimization Approach For Classification Of Occupational Low Back Disorder Risks. Human Factors And Ergonomics İn Manufacturing 19(1):1–14, 2009.CrossRefGoogle Scholar
  17. 17.
    Green, M., Björk, J., Forberg, J., Ekelund, U., et al., Comparison Between Neural Networks And Multiple Logistic Regression To Predict Acute Coronary Syndrome İn The Emergency Room. Artificial İntelligence İn Medicine 38(3):305–318, 2006.CrossRefPubMedGoogle Scholar
  18. 18.
    Conforti, D., and Guido, R., Kernel-Based Support Vector Machine Classifiers For Early Detection Of Myocardial İnfarction. Optimization Methods And Software 20(2-3):401–413, 2005.CrossRefGoogle Scholar
  19. 19.
    Abe, S., (2005). Support vector machines for pattern classification, 2nd Edn, SpringerGoogle Scholar
  20. 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. 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. 22.
    Vadicherla, D., and Sonawane, S., Classification Of Heart Disease Using Svm And Ann. Ijrcct 2(9):693–701, 2013.Google Scholar
  23. 23.
    Chitra, R., and Seenivasagam, V., Heart Disease Prediction System Using Supervised Learning Classifier. Bonfring International Journal Of Software Engineering And Soft Computing 3(1):01–07, 2013.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Göksu Bozdereli Berikol
    • 1
    • 2
    • 3
  • Oktay Yildiz
    • 1
    • 2
    • 3
  • İ. Türkay Özcan
    • 1
    • 2
    • 3
  1. 1.Karaman Public Hospital, Department of Emergency Medicine KARAMANKaraman Public HospitalKaramanTurkey
  2. 2.Computer Engineering DeptGazi University Faculty of EngineeringAnkaraTurkey
  3. 3.Faculty of Medicine, Dept. of Cardiology MERSİNMersin University Research and Training HospitalMersinTurkey

Personalised recommendations