An Explainable Deep Learning Framework for Cardiovascular Risk Prediction Using ECG and Imaging Data

Cardiovascular disease (CVD) is now the number one cause of death globally, and for its timely and reliable diagnosis, it is required for the prevention and treatment of CVD. In this work, we propose an explainable deep learning model for CVD risk prediction with 12-lead electrocardiogram (ECG) recordings in the PTB-XL dataset. A 1D-CNN is used to extract features from ECG signals automatically, and this process is further enhanced by XAI methods, such as SHAP and Grad-CAM, to increase interpretability and clinical transparency. Noise filtering, signal segmentation, normalizing and data augmentation enable input pre-processing to be clean and consistent for robust learning. The effectiveness of the proposed model has been validated through holdout and cross-validation procedures, yielding good classification performance. The model achieves high accuracy, precision, recall, and F1-score in distinguishing CVD cases from normal ones. It has provided a reliable and scalable system for real-time cardiac risk estimation and serious decision-making by unifying diagnostic precision and interpretability.