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Diagnosis of Acute Coronary Syndrome with a Support Vector Machine

  • Transactional Processing Systems
  • Published:
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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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References

  1. Nichols, M., Townsend, N., Scarborough, P., and Rayner, M., Cardiovascular Disease İn Europe: Epidemiological Update. Eur. Heart J. 34(39):3028–3034, 2013.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  PubMed  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.

  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.

    Article  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.

    Article  PubMed  Google Scholar 

  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.

    Article  CAS  PubMed  Google 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 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  CAS  PubMed  Google Scholar 

  15. Aj, S., and Schölkopf, B., A Tutorial On Support Vector Regression. Neurocolt Technical Report 14(3):199–222, 2004.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  19. Abe, S., (2005). Support vector machines for pattern classification, 2nd Edn, Springer

  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’) Pattaya

  22. Vadicherla, D., and Sonawane, S., Classification Of Heart Disease Using Svm And Ann. Ijrcct 2(9):693–701, 2013.

    Google Scholar 

  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.

    Article  Google Scholar 

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Authors and Affiliations

  1. Karaman Public Hospital, Department of Emergency Medicine KARAMAN, Karaman Public Hospital, Karaman, Turkey

    Göksu Bozdereli Berikol, Oktay Yildiz & İ. Türkay Özcan

  2. Computer Engineering Dept, Gazi University Faculty of Engineering, Ankara, Turkey

    Göksu Bozdereli Berikol, Oktay Yildiz & İ. Türkay Özcan

  3. Faculty of Medicine, Dept. of Cardiology MERSİN, Mersin University Research and Training Hospital, Mersin, Turkey

    Göksu Bozdereli Berikol, Oktay Yildiz & İ. Türkay Özcan

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  1. Göksu Bozdereli Berikol
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  2. Oktay Yildiz
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  3. İ. Türkay Özcan
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Correspondence to Göksu Bozdereli Berikol.

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This article is part of the Topical Collection on Transactional Processing Systems

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Berikol, G.B., Yildiz, O. & Özcan, İ.T. Diagnosis of Acute Coronary Syndrome with a Support Vector Machine. J Med Syst 40, 84 (2016). https://doi.org/10.1007/s10916-016-0432-6

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  • DOI: https://doi.org/10.1007/s10916-016-0432-6

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