CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS

Burçin Kurt; İlknur Buçan Kıkrbir

doi:10.18038/estubtda.1025092

EN TR

CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS

Abstract

Heart attack which is the main cause of death for both men and women is the leader among deaths due to heart diseases. Therefore, early diagnosis is very important for patients who are having a heart attack. Therefore, the study aimed to develop a clinical decision support system for the diagnosis of a heart attack to help physicians. In the study, variables were obtained accompanied by physicians by statistical analysis methods, where the optimum variables were selected from these variables considering the patient’s unconscious state in some cases. Different decision models were developed using probit regression, decision tree, SVM, and ANN methods. As a result, the developed clinical decision support models for heart attack diagnosis were compared and evaluated. Consequently, the best diagnosis model was obtained using ANN with selected variables. In addition to these, the proposed study is significantly noticed with a sensitivity of 98% and specificity of 93.7% for heart attack diagnosis with optimum variables compared to similar studies in the literature. By using the proposed decision support system, it is possible to determine whether a patient has a heart attack or not and help the physician in the process of diagnosis of a heart attack.

Keywords

Heart attack, Machine learning, Clinical decision support system

CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS

Abstract

Heart attack which is the main cause of death for both men and women is the leader among deaths due to heart diseases. Therefore, early diagnosis is very important for patients who are having a heart attack. Therefore, the study aimed to develop a clinical decision support system for the diagnosis of a heart attack to help physicians. In the study, variables were obtained accompanied by physicians by statistical analysis methods, where the optimum variables were selected from these variables considering the patient’s unconscious state in some cases. Different decision models were developed using probit regression, decision tree, SVM, and ANN methods. As a result, the developed clinical decision support models for heart attack diagnosis were compared and evaluated. Consequently, the best diagnosis model was obtained using ANN with selected variables. In addition to these, the proposed study is significantly noticed with a sensitivity of 98% and specificity of 93.7% for heart attack diagnosis with optimum variables compared to similar studies in the literature. By using the proposed decision support system, it is possible to determine whether a patient has a heart attack or not and help the physician in the process of diagnosis of a heart attack.

Keywords

Heart attack, Machine learning, Clinical decision support system

References

[1] Türkiye cardiovascular disease prevention and control program action plan. Ministry of Health, Public Health Institution of Türkiye, 988, 2015.
[2] Storrow AB, Gibler WB. Chest pain centers: diagnosis of acute coronary syndromes. Ann Emerg Med 2000; 35: 449-461.
[3] Doğan Ş, Türkoğlu İ, Yavuzkır M. Heart attack detection from cardiac by using decision trees. eJournal of New World Sciences Academy 2007; 2: 39-50.
[4] Mair J, Smidt J, Lechleitner P, Dienstl F, Puschendorf B. A decision tree for the early diagnosis of acute myocardial infarction in nontraumatic chest pain patients at hospital admission. The Journal of Emergency Medicine 1995; 14: 1502-1509.
[5] Dangare CS, Apte SS. A data mining approach for prediction of heart disease using neural networks. International Journal of Computer Engineering and Technology 2012; 3: 30-40.
[6] Ghumbre S, Patil C, Ghatol A. Heart disease diagnosis using support vector machine. International Conference on Computer Science and Information Technology (ICCSIT'2011) 17-18 December 2011; Pattaya, Thailand, 84-88.
[7] Garson GD. Probit Regression and Response Models. Statistical Associates Publishers, 2013.
[8] Razzaghi M. The probit link function in generalized linear models for data mining applications. Journal of Modern Applied Statistical Methods 2013; 12(1): 164-169.
[9] Hosmer D, Lemeshow S. Applied Logistic Regression (Second Edition). New York: John Wiley & Sons, Inc, 2000.
[10] Long JS. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage Publications, 1997.

[11] Smithson M, Merkle EC. Generalized Linear Models for Categorical and Continuous Limited Dependent Variables. New York, USA: CRC Press, 2013.
[12] Yazıcı B, Alpu Ö, Yang Y. Comparison of goodness-of-fit measures in probit regression model. Communications in Statistics-Simulation and Computation 2007; 36: 1061–1073.
[13] Aldrich J.H., Nelson F.D.. Probability, logit, and probit models (Quantitative Applications in the Social Sciences). California, US: Sage Publications, 1984.
[14] McFadden D. Quantitative methods for analyzing travel behavior of individuals: some recent developments. London, England: D. Hensher and P. Stopher (eds.), Behavioural Travel Modeling, 1978.
[15] Veall MR., Zimmerman KF. Evaluating pseudo-R2's for binary probit models. Quality and Quantity 1994; 28: 151–164.
[16] Güner N, Çomak E. Predicting the success of engineering students in mathematics lessons using support vector machines. Pamukkale University Journal of Engineering Sciences 2011; 2: 87-96.
[17] Kavzoğlu T, Çölkesen İ. Investigation of the effects of kernel functions on the classification of satellite images with support vector machines. Map Journal 2010; 144: 73-82.
[18] Yılmaz Akşehirli Ö, Ankaralı H, Aydın D, Saraçlı Ö. An alternative approach to medical prediction: Support vector machines. Türkiye Clinics Journal of Biostatistics 2013; 1: 19-28.
[19] Ma Y, Guo G. Support Vector Machines Applications. MN, USA: Springer, 2014.
[20] Berry MJA, Linoff GS. Mastering Data Mining: The Art and Science of Customer Relationship. 1st ed. New York, US: Wiley Computer Publishing, 2000.
[21] Balaban ME, Kartal E. Data mining and machine learning basic algorithms and applications with R language. Istanbul, Türkiye: Caglayan Bookstore, 2015.
[22] Çalış A, Kayapınar S, Çetinyokuş T. An application on computer and internet security with decision tree algorithms in data mining. Journal of Industrial Engineering 2014; 2-19.
[23] Öztemel E. Artificial neural networks. Third edition. İstanbul, Türkiye: Papatya Publishing Education Inc. 2012.
[24] Gönül Y, Ulu Ş, Bucak A. Artificial neural networks and their use in clinical research. Journal of General Medicine 2015; 25: 104-111.
[25] Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications 2011; 17: 43-48.
[26] Hazra A, Mandal SK, Gupta A, Mukherjee A and Mukherjee A. Heart disease diagnosis and prediction using machine learning and data mining techniques: a review. Advances in Computational Sciences and Technology 2017; 10 (7): 2137-2159.
[27] Mujawar SH, Devale PR. Prediction of heart disease using modified k-means and by using naive bayes. International Journal of Innovative Research in Computer and Communication Engineering 2015; 3(10): 10265-10273.
[28] Aydın S, Ahanpanjeh M, Mohabbatiyan S. Comparison and evaluation of data mining techniques in the diagnosis of heart disease. International Journal on Computational Science & Applications (IJCSA) 2016; 6(1): 1-15.
[29] Florence S, Bhuvaneswari Amma NG, Annapoorani G and Malathi K, “Predicting The Risk of Heart Attacks using Neural Network and Decision Tree”, International Journal of Innovative Research In Computer and Communication Engineering, ISSN (Online): 2320-9801, November 2014; Vol. 2, Issue 11, pp. 7025-7028.

Details

Primary Language

English

Subjects

Decision Support and Group Support Systems

Journal Section

Research Article

Authors

Burçin Kurt ^*
0000-0001-5781-2382
Türkiye

İlknur Buçan Kıkrbir
0000-0002-0611-0118
Türkiye

Publication Date

June 21, 2023

Submission Date

November 17, 2021

Acceptance Date

April 6, 2023

Published in Issue

Year 2023 Volume: 24 Number: 2

DOI

https://doi.org/10.18038/estubtda.1025092

IZ

https://izlik.org/JA45AW24NK

APA

Kurt, B., & Buçan Kıkrbir, İ. (2023). CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, 24(2), 107-120. https://doi.org/10.18038/estubtda.1025092

AMA

1.Kurt B, Buçan Kıkrbir İ. CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS. Estuscience - Se. 2023;24(2):107-120. doi:10.18038/estubtda.1025092

Chicago

Kurt, Burçin, and İlknur Buçan Kıkrbir. 2023. “CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 24 (2): 107-20. https://doi.org/10.18038/estubtda.1025092.

EndNote

Kurt B, Buçan Kıkrbir İ (June 1, 2023) CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 24 2 107–120.

IEEE

[1]B. Kurt and İ. Buçan Kıkrbir, “CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS”, Estuscience - Se, vol. 24, no. 2, pp. 107–120, June 2023, doi: 10.18038/estubtda.1025092.

ISNAD

Kurt, Burçin - Buçan Kıkrbir, İlknur. “CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 24/2 (June 1, 2023): 107-120. https://doi.org/10.18038/estubtda.1025092.

JAMA

1.Kurt B, Buçan Kıkrbir İ. CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS. Estuscience - Se. 2023;24:107–120.

MLA

Kurt, Burçin, and İlknur Buçan Kıkrbir. “CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 24, no. 2, June 2023, pp. 107-20, doi:10.18038/estubtda.1025092.

Vancouver

1.Burçin Kurt, İlknur Buçan Kıkrbir. CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS. Estuscience - Se. 2023 Jun. 1;24(2):107-20. doi:10.18038/estubtda.1025092

CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS

Abstract

Keywords

CLINICAL DECISION SUPPORT SYSTEM FOR EARLY DIAGNOSIS OF HEART ATTACK USING MACHINE LEARNING METHODS

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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