Prediction of Acute Heart Attack using Logistic Regression (Case Study: A Hospital in Iran)

Document Type : Research Paper

Authors

Department of Industrial Engineering, South Tehran Branch,Islamic Azad University, Tehran, Iran

Abstract

Acute myocardial infarction is the most important reason of mortality in Iran. More than half of these deaths occur without the patient even reaching to a hospital. There is the evidence that patients with better knowledge of the symptoms of MI will seek help earlier. The purpose of this study is to determine how well a predictive model will perform based solely upon patient-reportable clinical history factors, without using diagnostic tests or physical exam findings. We use 28 patient-reportable history factors that are included as potential covariates in our models. Using a derivation data set of 663 patients, we build three logistic regression models and one decision tree model to estimate the likelihood of acute coronary syndrome based upon patient-reportable clinical history factors only. The best performing logistic regression model have 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 is selected as the main features. A decision tree model has a C-index of 0.938. The variables, shortness of breath, palpitations, edema, sweats, left chest pain, age, severe chest pain and nausea are selected as the main features. This model can have important utility in the applications outside of a hospital setting when objective diagnostic test information is not yet available. Given the very high mortality from MI in the Iran, even a small reduction in median time from onset of symptoms to treatment can translate into a substantial number of lives saved.

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References

1-    Samavat, T., Hojjatzadeh, A., Shams, M., Afkhami, A., Mahdavi, A., Bashti, Sh., Pouraram, H., Ghotbi, M., Rezvani, A. (1391). “Prevention and control of cardiovascular disease (for government employees).”second edition.
2-    Selker, H.P., Griffith, J.L., Patil, S., Long, W.J., D'Agostino, R.B. (1995). “A comparison of performance of mathematicalpredictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients. ”J. Investig. Med, Vol. 43, PP. 468-476.
3-    Wang, S.J., Ohno-Machado, L., Fraser, H.S., Lee Kennedy, R. (2001). “Using patient-reportable clinicalhistory factors to predict myocardial infarction. ”Computers in Biology and Medicine, Vol, 31, PP. 1-13.
4-    Kennedy, R.L., Burton, A.M., Fraser, H.S.,McStay, L.N., Harrison, R.F. (1996).“Early diagnosisof acutemyocardial infarction using clinical and electrocardiographic data at presentation: derivationand evaluation of logistic regression models. ”Eur. Heart J., Vol. 17, PP. 1181-1191.
5-    Do, D., West, J.A., Morise, A., Atwood, E.,Froelicher, V. (1997). “A consensus approach to diagnosingcoronary artery disease based on clinical and exercise test data. ”Chest, Vol. 111, PP. 1742- 1749.
6-    Haraldsson, H.,Edenbrandt, L.,Ohlsson, M. (2004).“Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks. ”Artificial Intelligence in Medicine,Vol. 32, PP. 127-136.
7-    Biglarian, A., Hajizadeh, E., Kazemnejad, A., Zayeri, F. (2010). “Determining of prognostic factors in gastric cancer  patients using artificial neural networks. ”Asian Pac J Cancer Prev, Vol.11(2), PP. 533-536.
8-    Anooj, P.K. (2012). “Clinical decision support system: Risk level prediction of heart disease usingweighted fuzzy rules. ”Journal of King Saud University – Computer and Information Sciences,Vol. 24, PP. 27-40.
9-    Rajeswari, K., Vaithiyanathan, V.,Neelakantan, T.R. ( 2012 ). “ Feature Selection in Ischemic Heart DiseaseIdentification using Feed Forward Neural Networks. ”Procedia Engineering, Vol. 41, PP. 1818 – 1823 .
10- Atkov, O.YU.,Gorokhova, S.G., Sboev, A.G., Generozov, E.V., Muraseyeva, E.V., Moroshkina, S.Y.,  Cherniy, N.N. (2012). “Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. ”Journal of Cardiology, Vol. 59, PP. 190-194.
11- Safdari, R.,GhaziSaeedi, M.,Arji, G.,Gharooni, M.,Soraki, M.,Nasiri, M. (2013).“A model for predictingmyocardial infarction using data mining techniques. ”Iranian journal of medical informatics, vol 2, issue 4.
12- Suchithra, Maheswari, P.U. (2014). “Survey on Clinical Decision Support System for DiagnosingHeartDisease. ”International Journal of Computer Science and Mobile Computing, vol 3, Issue 2, PP. 21-28 .
13- Heden, B., Ohlin, H.,Rittner, R.,Edenbrandt, L. (1997).“Acute myocardial infarction detected in the 12-lead ECG byartificial neural networks. ”Circulation, Vol. 96, PP. 1798-1802.
14- Harrison, R.F.,Kennedy, R.L. (2005). “Artificial neural network models for prediction of acutecoronary syndromes using clinical data from the time of presentation. ”Ann Emerg Med, Vol. 46, PP. 431-439.
15- Austin, P.C.,Tu, J.V., Ho, J.E., Levy, D., Lee, D.S. (2013). “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, Vol. 66, PP. 398-407.
16- Chen, C.M., Hsu, C.Y., Chiu, H.W., Rau, H.H. (2011). “Prediction of survival in patients with liver cancerusing artificial neural networks and classification and regression trees. ”IN Natural Computation (ICNC),Seventh International Conference on Vol. 2, pp. 811-815. IEEE.
17- Vinterbo, S.,Ohno-Machado, L. (1999). “A genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction. ”Proceedings of AMIA Annual Fall Symposium, pp. 984-988.
18- Kurt, I., Ture, M., Kurum, AT. (2008). “Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. ”Expert SystAppl, Vol. 34, PP. 366-374.
19- Zweig, M.H., Campbell, G. (1993). “Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. ”Clin. Chem., Vol. 39, PP. 561-577.
20- Scott, M. (2001). “Applied logistic Regression Analysis. ”Second  Publication,Sage Publication.
Advances in Industrial Engineering
Volume 50, Issue 1
بهار و تابستان
April 2016
Pages 109-119

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