A Novel CNN-RNN-LSTM Framework for Predictive Cardiovascular Diagnostics of Aortic Stenosis in a Large Scale 12-Lead ECG Dataset

Abstract

Aortic stenosis (AS) is a disease of the valve between the heart and aorta and may lead to heart failure if left untreated; it is one of the significant valvular heart diseases caused by the narrowing of this valve. Conventional diagnostic techniques are invasive and require resources. Machine learning and deep learning approaches for the non-invasive identification of AS were investigated using an extensive 12-lead ECG dataset of 10,646 patient records. A range of models was assessed for diagnostic performance, including Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a hybrid CNN-LSTM model, and a hybrid CNN-RNN-LSTM model. The results indicate that SVM and RF had 74% and 76% accuracy, respectively, while the CNN model improved the accuracy to 80%. The accuracy of the LSTM model was 82%, and the accuracy of the CNN-LSTM hybrid model was 85%. The most proficient model was the hybrid CNN-RNN-LSTM model, which had an accuracy of 87% and high precision, recall, and F1 scores. The promise of deep learning, especially hybrid models, for advancing non-invasive diagnostic techniques for aortic stenosis, including those that could greatly aid early detection and improve patient outcomes in a clinical setting, is highlighted by this research.