Deep learning Methods for ECG-Based Heart Disease Detection

Cardiovascular disease (CVD) continues to be a primary cause of death globally, and early detection plays a critical role in improving patient outcomes. This research introduces the development of a deep learning model designed to automatically categorize heart diseases using Electrocardiogram (ECG) data. The model utilizes a 1D Convolutional Neural Network (CNN) structure and makes use of the MIT-BIH Arrhythmia dataset from Physionet. The dataset was split into training, validation, and testing subsets. Our proposed design incorporates convolutional layers, max-pooling, ReLU activation functions, and dropout layers to prevent overfitting. Comparative assessment against conventional methods such as logistic regression and Support Vector Machines (SVM) shows superior performance, achieving an accuracy of 98.29%, recall of 87.60%, precision of 93.75%, and F1 score of 90.37%. The potential of deep learning to enhance the accuracy and efficiency of diagnosing CVD from ECG data is highlighted in this study, introducing a reliable tool for clinical application.