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Journals / The Journal of Cognitive Systems / 2020 / Volume: 5 Issue: 2

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

Zeynep Tunç(İNÖNÜ ÜNİVERSİTESİ, TIP FAKÜLTESİ, TEMEL TIP BİLİMLERİ BÖLÜMÜ, BİYOİSTATİSTİK VE TIP BİLİŞİMİ ANABİLİM DALI)0000-0001-7956-9272
İpek Balıkçı Çiçek(İNÖNÜ ÜNİVERSİTESİ, TIP FAKÜLTESİ, TEMEL TIP BİLİMLERİ BÖLÜMÜ, BİYOİSTATİSTİK VE TIP BİLİŞİMİ ANABİLİM DALI)0000-0002-3805-9214
Emek Güldoğan(İNÖNÜ ÜNİVERSİTESİ)0000-0002-5436-8164
Pages
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Publication Date
2020-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In addition, the importance values of the factors associated with the model used were estimated.

📌 3 Key Takeaways

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Objective & Hypothesis: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.
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Key Findings & Data:The performance of the method used was evaluated with accuracy, sensitivity, selectivity, positive predictive value, negative predictive value.
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Conclusion & Impact:In addition, the importance values of the factors associated with the model used were estimated.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Heart attack · machine learning · deep learning

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.

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Cite This Article

Tunç, Z., Çiçek, İ. B., Güldoğan, E. (2020). PERFORMANCE EVALUATION OF THE DEEP LEARNING MODELS IN THE CLASSIFICATION OF HEART ATTACK AND DETERMINATION OF RELATED FACTORS. The Journal of Cognitive Systems, Volume: 5 Issue: 2. https://ajindex.com/articles/1551993

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