Applying the Regression Technique for Prediction of the Acute Heart Attack
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:71189", author = "Paria Soleimani and Arezoo Neshati", title = "Applying the Regression Technique for Prediction of the Acute Heart Attack", abstract = "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, acute heart attacks,
prediction, logistic regression.", volume = "9", number = "11", pages = "767-5", }
