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Updated: Apr 23, 2026
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Quantum-inspired cardiac risk assessment using hybrid CSAGGO-Q-SpinalNet algorithm for precise heart disease
W Ancy Breen1, S Muthu Vijaya Pandian2, M Muthukrishnaveni3
1Department of CSE, SRM University, Chennai, Tamilnadu, India.
Insights
This study introduces an intelligent system for accurate heart disease prediction using advanced machine learning. The novel CSAGGO-Q-SpinalNet framework significantly improves diagnostic accuracy and provides explainable insights for clinical use.
Area of Science:
- Cardiovascular disease research
- Artificial intelligence in healthcare
- Machine learning for medical diagnosis
Background:
- Cardiovascular disease is a leading global cause of mortality.
- Early diagnosis is crucial but hindered by data imbalance and complexity.
- Sophisticated intelligent systems are needed for accurate heart disease prediction.
Purpose of the Study:
- To develop an intelligent system for enhanced heart disease prediction.
- To integrate advanced machine learning techniques for improved classification accuracy.
- To provide explainable insights for clinical decision-making.
Main Methods:
- Data preprocessing with Principal Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE).
- Feature selection using Least Absolute Shrinkage and Selection Operator (LASSO).
- Hybrid classification model combining Capuchin Search Algorithm Graylag Goal Optimization (CSAGGO) and Quantum-SpinalNet (Q-SpinalNet).
- Interpretability analysis using SHapley Additive exPlanations (SHAP).
Main Results:
- The CSAGGO-Q-SpinalNet model achieved 98.44% accuracy, 96.89% sensitivity, and 96.83% specificity on the Cleveland dataset.
- Demonstrated low error rates: 4.24% False Positive Rate (FPR), 4.38% False Negative Rate (FNR).
- SHAP values provided actionable insights into feature influence on predictions.
Conclusions:
- The proposed intelligent system offers a robust, efficient, and explainable solution for heart disease prediction.
- The hybrid CSAGGO-Q-SpinalNet framework outperforms existing methods.
- This system shows significant promise for early diagnosis and personalized treatment in clinical settings.
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.
