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Applying the Regression Technique for Prediction of the Acute Heart Attack

Authors: Paria Soleimani, Arezoo Neshati


Myocardial infarction is one of the leading causes of death in the world. Some of these deaths occur even before the patient reaches the hospital. Myocardial infarction occurs as a result of impaired blood supply. Because the most of these deaths are due to coronary artery disease, hence the awareness of the warning signs of a heart attack is essential. Some heart attacks are sudden and intense, but most of them start slowly, with mild pain or discomfort, then early detection and successful treatment of these symptoms is vital to save them. Therefore, importance and usefulness of a system designing to assist physicians in early diagnosis of the acute heart attacks is obvious. The main purpose of this study would be to enable patients to become better informed about their condition and to encourage them to seek professional care at an earlier stage in the appropriate situations. For this purpose, the data were collected on 711 heart patients in Iran hospitals. 28 attributes of clinical factors can be reported by patients; were studied. Three logistic regression models were made on the basis of the 28 features to predict the risk of heart attacks. The best logistic regression model in terms of performance had a C-index of 0.955 and with an accuracy of 94.9%. The variables, severe chest pain, back pain, cold sweats, shortness of breath, nausea and vomiting, were selected as the main features.

Keywords: coronary heart disease, Logistic Regression, prediction, Acute heart attacks

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[1] T. Samavat, A. Hojjatzadeh, M. Shams, A. Afkhami, A. Mahdavi, Sh. Bashti, H. Pouraram, M. Ghotbi, A. Rezvani, Prevention and control of cardiovascular disease (for government employees). Second edition (2012).
[2] H. P. Selker, J. L. Griffith, S. Patil, W. J. Long, R. B. D'Agostino, A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients, J. Investig. Med, 43 (1995) 468-476.
[3] R. L. Kennedy, A.M. Burton, H.S. Fraser, L.N. McStay, R.F. Harrison, Early diagnosis of acute myocardial infarction using clinical and electrocardiographic data at presentation: derivation and evaluation of logistic regression models, Eur. Heart J. 17 (1996) 1181-1191.
[4] D. Do, J. A. West, A. Morise, E. Atwood, V. Froelicher, A consensus approach to diagnosing coronary artery disease based on clinical and exercise test data, Chest 111 (1997) 1742- 1749.
[5] S. J. Wang, L. Ohno-Machado, H. S. F. Fraser, R. Lee Kennedy, Using patient-reportable clinical history factors to predict myocardial infarction: Computers in Biology and Medicine, 31 (2001) 1-13.
[6] H. Haraldsson, L. Edenbrandt, M. Ohlsson, Detecting acute myocardial infarction in the 12- lead ECG using Hermite expansions and neural networks, Artificial Intelligence in Medicine, 32 (2004) 127-136.
[7] R. F. Harrison, R. L. Kennedy, Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation, Ann Emerg Med, 46 (2005) 431-439.
[8] P. K. Anooj, Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules, Journal of King Saud University – Computer and Information Sciences, 24 (2012) 27-40.
[9] K. Rajeswari, V. Vaithiyanathan, T. R. Neelakantan, Feature Selection in Ischemic Heart Disease Identification using Feed Forward Neural Networks, Procedia Engineering 41 (2012) 1818 – 1823 .
[10] R. Safdari, M. GhaziSaeedi, G. Arji, M. Gharooni, M. Soraki, M. Nasiri, A model for predicting myocardial infarction using data mining techniques, Iranian journal of medical informatics, (2013) vol. 2, issue 4.
[11] Suchithra, P. U. Maheswari, Survey on Clinical Decision Support System for Diagnosing Heart Disease, IJCSMC, (2014) vol. 3, Issue 2, 21-28 .
[12] O. Y. U. Atkov, S. G. Gorokhova, A. G. Sboev, E. V. Generozov, E. V. Muraseyeva, S.Y . Moroshkina, N. N. Cherniy, Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters, Journal of Cardiology, 59 (2012) 190-194.
[13] P. C. Austin, J. V. Tu, J. E. Ho, D. Levy, D. S. Lee, Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes, Journal of Clinical Epidemiology 66 (2013) 398-407.
[14] Kurt I., Ture M., Kurum A. T. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert SystAppl (2008) 34(1) 366- 374.
[15] M. Scott, Applied logistic Regression Analysis, Second Publication, Sage Publication (2001).
[16] S. Dreiseitl, L. Ohno-Machado, S. Vinterbo, Evaluating variable selection methods for diagnosis of myocardial infarction, Proceedings of AMIA Annual Fall Symposium (1999) pp. 246-250.
[17] M. H. Zweig, G. Campbell, Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine (published erratum appears in Clin. Chem. 39(8) (1993) 1589), Clin. Chem. 39 (1993) 561-577.