Quantum-inspired cardiac risk assessment using hybrid CSAGGO-Q-SpinalNet algorithm for precise heart disease prediction

Abstract

Cardiovascular disease remains the leading cause of mortality globally, with escalating risk factors and increasing pressure on the healthcare system. Despite the critical importance of early diagnosis, it is impeded by challenges, such as data imbalance, feature complexity, and variability in diagnostic processes. These challenges necessitate the development of sophisticated intelligent systems to ensure the accurate and timely prediction of heart disease. This work presents an intelligent system that integrates advanced machine learning techniques for heart disease prediction, employing the capuchin search algorithm graylag goal optimization (CSAGGO) and Quantum-SpinalNet (Q-SpinalNet) for enhanced classification. The methodology begins with data preprocessing using Principal Component Analysis (PCA) and synthetic minority oversampling technique (SMOTE) to address issues of dimensionality and class imbalance. An improved Fuzzy C-Means Gaussian Mixture Model (FCM-GMM) was utilized for clustering, while the Least Absolute Shrinkage and Selection Operator (LASSO) identified the most informative features. To enhance interpretability, SHapley Additive exPlanations (SHAP) values were employed to elucidate the influence of individual features on predictions, providing actionable insights for healthcare professionals. The hybrid CSAGGO-Q-SpinalNet framework surpasses the existing methods, offering a robust, efficient, and explainable solution for heart disease prediction. The proposed system achieved exceptional performance metrics, including 98.44% accuracy, 96.89% sensitivity, 96.83% specificity, and 96.22% precision on the Cleveland dataset. Additionally, the model demonstrated low error rates with a 4.24% false positive rate (FPR), 4.38% false negative rate (FNR), and 4.05% false discovery rate (FDR). This system holds significant promise for real-world clinical applications by facilitating early diagnosis and personalized treatment strategies.