Heart disease classification based on performance measures using a deep learning model

Abstract

Heart disease, which is one of the most common diseases in the world, is expected to remain the leading cause of mortality on a global scale. Therefore the aim of this study is to classify heart disease using a deep learning approach in an open-access dataset that includes data from patients with and without heart disease. In this study, a deep learning model was applied to an open-access data set containing the data of patients with and without heart disease. The performance of the method used was evaluated with the performance criteria of specificity, sensitivity, accuracy, positive predictive value, and negative predictive value. Specificity, sensitivity, accuracy, positive predictive value and negative predictive value from the performance criteria obtained from the model were calculated as 0.946, 0.903, 0.9245, 0.9436 and 0.907, respectively. As a result of the findings obtained from the study, it was seen that the data set we discussed was successfully classified with the deep learning model used. With this obtained high classification performance, the factors associated with the disease can be revealed.

Keywords

• Artificial intelligence
• Heart disease
• Machine learning
• Deep learning
• Classifiction

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