A novel hybrid deep learning and reinforcement learning framework for multimodal cardiovascular disease prediction
Islam D. S. Aabdalla1 and D. Vasumathi2
Research Scholar, Department of Computer Science and Engineering,Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad, Telangana,India1
Professor, Department of Computer Science and Engineering,Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad, Telangana,India2
Professor, Department of Computer Science and Engineering,Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad, Telangana,India2
Corresponding Author : Islam D. S. Aabdalla
Recieved : 04-December-2024; Revised : 30-May-2025; Accepted : 15-June-2025
Abstract
Heart diseases continue to be the leading cause of mortality, emphasising the urgent need for advanced and accurate diagnostic systems. In this work, we present an integrated framework that synergistically integrates deep learning and reinforcement learning (RL) for the classification of cardiovascular diseases (CVDs) using multimodal physiological signals, specifically electrocardiograms (ECGs) and phonocardiograms (PCGs). The proposed architecture leverages Convolutional neural networks (CNNs) to extract spatial features and recurrent neural networks (RNNs) to capture temporal dynamics. At the same time, RL is employed to optimise decision-making and adaptively improve classification accuracy. To effectively fuse multimodal data, we implement and evaluate early, late, and hybrid fusion strategies. The framework is validated on the benchmark EPHNOGRAM dataset, achieving a peak detection accuracy of 94%. Comparative experiments demonstrate that our hybrid LSTM-BiLSTM model outperforms existing state-of-the-art techniques. These results underscore the potential of the proposed framework as a robust and intelligent system for automated CVD diagnosis, paving the way for future improvements in clinical decision support powered by deep learning and RL.
Keywords
Deep learning, Reinforcement learning, Multimodal fusion, Heart disease, and Machine learning.
Cite this article
Aabdalla IDS, Vasumathi D. A novel hybrid deep learning and reinforcement learning framework for multimodal cardiovascular disease prediction. International Journal of Advanced Computer Research. 2025;15(73):25-38. DOI : 10.19101/IJACR.2024.1466030
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