PERFORMANCE EVALUATION OF THE DEEP LEARNING MODELS IN THE CLASSIFICATION OF HEART ATTACK AND DETERMINATION OF RELATED FACTORS

Year 2020, Volume: 5 Issue: 2, 99 - 103, 31.12.2020

Zeynep Tunç İpek Balıkçı Çiçek , Emek Güldoğan

Abstract

Aim: The aim of this study is to classify the condition of having a heart attack and determine the related factors by applying the deep learning method, one of the machine learning methods, on the open-access data set.

Materials and Methods: In this study, deep learning method was applied to an open-access data set named “Health care: Data set on Heart attack possibility”. The performance of the method used was evaluated with accuracy, sensitivity, selectivity, positive predictive value, negative predictive value. The factors associated with having a heart attack were determined by deep learning methods and the most important factors were identified.

Results: Accuracy, sensitivity, specificity, positive predictive value and negative predictive value obtained from the model were 0.814, 0.804, 0.823, 0.809 and 0.834 respectively. The most important 3 factors that may be associated with having a heart attack were obtained as thal, age, ca.

Conclusion: The findings obtained from this study showed that successful predictions were obtained in the classification of having a heart attack by the deep learning method used. In addition, the importance values of the factors associated with the model used were estimated.

Keywords

Heart attack, machine learning, deep learning, classification, variable importance

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