Enhancing Transparency in Cardiovascular Disease Prediction Using CNN-MLP and Explainable AI Framework for Sustainable Health Care

Abstract

Cardiovascular disease (CVD) requires more specific and comprehensible diagnostic tools, as it causes numerous deaths in the world. This study aims to provide a multimodal, comprehensive, effective, and clinically interpretable AI model for heart disease prediction. Sometimes medical practitioners may find it difficult to comprehend and trust the model's decision-making process, and the “black box” nature of these models limits their practical usefulness despite their high predictive accuracy. To address this challenge, the ongoing research proposes an explainable AI (XAI) framework that integrates a Multi-Layer Perceptron (MLP) and a two-dimensional Convolutional Neural Network (2D-CNN) with Layer-wise Relevance Propagation (LRP) for transparent, potent, and reliable cardiac disorder prediction. LRP reveals the most significant features for interpreting the model's output by assigning relevance scores to each input feature that leads to a particular prediction. Local and global explanations are generated to assist the stakeholders in understanding the rationale for the decision. The model achieves 98% accuracy on Electrocardiogram (ECG) data and 97% on Electronic Health Record (EHR) data, indicating strong generalization and high accuracy. While the proposed CNN-MLP with LRP framework achieved high performance and interpretability, several limitations must be acknowledged. To start with, the research was based on the publicly available dataset of ECG images and EHR data that might not be a complete reflection of the diversity of the real-world clinical populations. Consequently, the model has an unclear ability to generalize information. Second, the dataset size, though sufficient for proof-of concept, may still be limited for deep learning applications, and the lack of an independent test set could slightly overestimate performance. Third, the model was trained on static ECG images rather than raw time-series signals, potentially omitting temporal dependencies critical for certain cardiac diagnoses. Furthermore, Layer-wise Relevance Propagation (LRP) explanations are informative but require model architecture and can differ among implementations. Finally, the study was performed in a controlled, offline environment; in practice, it would take a lot of clinical validation, hospital systems integration, and evaluation of computational efficiency and interpretability. This research proposes a productive, interpretable diagnostic tool for cardiac disease that integrates Electrocardiogram (ECG) and Electronic Health Record (EHR) data, identifying the most informative features by assigning relevance scores. The CNN-MLP and LRP framework efficiently fuses diverse data for the early diagnosis of cardiovascular diseases in an understandable and privacy-aware manner. It improves clinical decision-making by providing relevance scores for multimodal features, thereby facilitating trustworthy AI-assisted diagnosis in cardiology.