A Deep Convolutional Model for Heart Disease Prediction based on ECG Data with Explainable AI

Abstract

Heart disease (HD) prediction is crucial in realizing the notion of intelligent healthcare owing to the exploding number of heart diseases being reported on a daily basis. However, in a domain like healthcare, accountability is key for a medical practitioner to completely adopt the decisions of an intelligent model. Accordingly, the proposed model develops a convolutional model for heart disease prediction based on ECG data in a supervised manner. Moreover, the easily accessible and economical ECG data is utilized in the model in the form of image data. The incorporation of ECG data as images has provided amazing results in the recent researches compared to being considered as signals. The architecture follows a stacked Convolutional Neural Network for extracting features from ECG images followed by fully connected network for classification. The evaluation of the proposed model on customized public datasets demonstrates its ability to achieve impressive outcomes by leveraging the characteristics of convolutional neural networks (CNNs) and supervised learning. Similarly, Explainability in the form of interpretability has been incorporated into the framework thus ensuring accountability of the model which is crucial in medical domain. Detailed experiments for identification of ideal model architecture are conducted. Further, local and vision based Explainability has been explored in detail using LIME and Grad-CAM. The model could achieve a precision, recall and f1-score of 0.982, 0.982, and 0.981 respectively proving the superiority of the model. Moreover, Explainability visualization based on popular algorithms for true positive and false positive results have shown promising results on the PhysioNet ECG dataset.