Background: Risk of developing cardiovascular diseases, in the world, is increasing day by day. Accordingly, the number of deaths due to heart attacks is quite remarkable. Early risk assessment and diagnosis of heart disease are vital to prevent heart attacks by providing effective treatment planning and evaluation of outcomes. When a patient with high risk of heart attack is not treated correctly, chances of survival may reduce dramatically. For this reason, artificial intelligence-assisted systems can support the decision of doctors and it can anticipate risk without fatal consequences.
Methods: In this study, individuals who has heart attack risks are predicted by using a proposed CNNs method. A set of medical data from patients with heart attacks and healthy individuals are provided from the UCI database. Reinforced deep learning and ANFIS architectures are also applied to the same problem in order to compare the results and put forth the efficiency of proposed method. In addition, ROC analysis and measurements of processing times for the applied methods were performed to reveal the performance, accuracy and efficiency of the study.
Results: The proposed CNNs method and other methods are tested and evaluated. The accuracy performance of the methods were 94.34% for the proposed CNNs method, 91.58% for the ANFIS, and 92.66% for the deep multilayer neural network. Highest accuracy has been obtained by using the proposed CNNs method, which is 94.34%. The reasons why the proposed CNNs method is better than other methods is the use of channel selection layer, the number of convolution and pooling layers, the filter size used in these layers, and the functions used in the loss and activation layers.
Conclusions: In the study, the channel selection formula is introduced in the proposed CNNs model to select the most discriminatory feature filters. Besides, the applicability of proposed CNNs method with images obtained from numerical data has been demonstrated. With the early prediction system proposed, it is now possible to take precautionary measures against possible cardiac arrest. In this study; a new method based on CNNs is proposed for early detection of possible heart attack, which is a great risk for human life. Different from studies in the literature, the channel selection formula is presented in the proposed CNNs method to select the most selective feature filters. Besides differently, it was used in the proposed CNNs method by converting all numerical data from dataset into 2D images. Afterwards, to show whether this the proposed method is applicable or not, the dataset which is numerical form was applied to other methods and compared.