Optimizing Cardiovascular Diagnosis using Deep Q-Learning Integrated CNN Model
DOI:
https://doi.org/10.15662/IJEETR.2026.0802023Keywords:
convolutional neural network, healthcare artificial intelligence, diagnostic prediction model, ODQ-CNN model, Artificial Neural NetworksAbstract
Cardiovascular disease remains a major global health concern, making early and accurate prediction essential for effective treatment. This study proposes an Optimized DeepQ Convolutional Neural Network (ODQ-CNN) that combines CNN with Deep Q-learning to improve feature extraction and classification. The model adaptively updates parameters such as learning rate and weights during training, enhancing performance. Evaluated on the Cleveland Heart Disease dataset, the proposed approach achieves a high accuracy of 98.7%, outperforming traditional models like SVM, Random Forest, and ANN. The integration of reinforcement learning with deep learning reduces overfitting and accelerates convergence, making it a reliable tool for intelligent healthcare decision support
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