A Hybrid Quantum-Inspired Deep Learning Framework with Bio-Inspired Optimization for Cardiovascular Disease Prediction
Abstract
The prognostication of cardiovascular diseases is paramount for the facilitation of early detection and enhancement of patient prognoses. It introduced a novel hybrid deep learning architecture that amalgamates Convolutional Neural Networks (CNN), Quantum Convolutional Neural Networks (Q-CNN), Long Short-Term Memory (LSTM) networks, Quantum-Inspired Long Short-Term Memory (Q-LSTM) models, Denoising Autoencoders (DAE), and Transformer Encoder–Decoder frameworks. The quantum models were innovatively structured by integrating unitary transformations and Hilbert space representations within traditional deep learning paradigms. Hyperparameter optimization, including learning rate, hidden unit count, dropout rates, and batch size, was executed utilizing the Greylag Goose Optimization (GGO) algorithm, which was meticulously chosen after initial benchmarking against conventional optimization techniques. These models underwent training and validation processes on a meticulously curated clinical dataset encompassing both demographic and clinical attributes, with preprocessing measures implemented to rectify missing data and address class imbalances. Among the array of assessed models, the GGO-optimized Q-LSTM exhibited superior performance, attaining an accuracy of 98.05% (95% CI: 96.8–99.2%), a precision of 1.00, a recall of 98.96%, an F1-score of 97.95%, and an AUC-ROC of 0.980. The DAE demonstrated an accuracy of 97.08% alongside an AUC-ROC of 0.989. Future research endeavors will focus on external validation and statistical significance testing to evaluate model generalization. Additionally, considerations regarding model interpretability through SHAP analysis and the practical deployment aspects (e.g., integration with Electronic Health Records) are thoroughly examined. This investigation underscores the assertion that the integration of deep learning methodologies, quantum-inspired modeling, and bio-inspired optimization strategies can markedly enhance predictive analytics for cardiovascular disease identification, while concurrently underscoring the critical importance of model interpretability and rigorous validation
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