A Hybrid Quantum based Self-Variational Onsager Neural Network with Leech Growth Algorithm-based Clinical Decision Support System for Accurate Heart Disease Detection
DOI:
https://doi.org/10.63949/Keywords:
- Heart Disease Detection,
- Leech Growth Algorithm,
- Medical Data Preprocessing,
- Z-score Min–Max Normalization,
- Clinical Decision Support
Abstract
Heart disease has been among the major causes of death in the world, and unless it is detected properly and on time, it cannot be treated effectively. Conventional methods of diagnosis generally have a problem with the complex clinical data, which results in misdiagnosis and delayed interventions, and that is why advanced computational methods are the ideal solution to this need. In this regard, the proposed research proposes the Hybrid Quantum based Self Variational Onsager Neural Network with Leech Growth Algorithm (HQ-SVONNet-LGA) to identify heart disease. At first, the Kaggle heart disease dataset is processed and prepared clinically according to the Z-score Min–Max Normalization (Z-M-MN) to provide consistency and reliability. HQ-SVONNet model represents intricate patterns in the data, whereas the Leech Growth Algorithm is an optimization of network parameters to achieve better convergence and accuracy. Experimental findings indicate that the proposed model works better than the existing approaches with a 99.1% accuracy rate, 98.7% precision, 99.5% specificity, and a low error rate of 0.9%. To summarize, HQ-SVONNet-LGA is a powerful, accurate, and effective model of reliable heart disease detection.
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References
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