Burçin KURT, İlknur BUÇAN KIKRBİR

33810

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

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.
Anahtar Kelimeler:

Heart attack, Machine learning, Clinical decision support system

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

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,

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Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering-Cover
  • ISSN: 2667-4211
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2000
  • Yayıncı: Eskişehir Teknik Üniversitesi
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