CardioMetaHybridOptimizer as a behaviorally adaptive multi-phase metaheuristic framework for interpretable cardiovascular disease diagnosis
Abstract
Cardiovascular disease prediction is delayed by high-dimensional clinical data and heteroginity. There is a need for decision-support system that can select relevant features. We propose a Cardio Meta Hybrid Optimizer (CMHO) framework designed to enhance feature selection and predictive accuracy in cardiac risk assessment.The CMHO framework integrates three metaheuristic algorithms-Lion Optimization (LO), Marine Predators Algorithm (MPA), and Manta Ray Foraging Optimization (MRFO)-enhanced with adaptive switching, dynamic mutation, and iterative local search (ILS). The framework was evaluated on five benchmark datasets: Cleveland, Hungarian, Statlog, Switzerland, and Long Beach VA. We uesd a CNN-LSTM architecture for classification, validated through stratified tenfold cross-validation with 10 independent repetitions. Performance was benchmarked against RFE, GA, PSO, GWO, and Lasso using ANOVA to confirm statistical significance. The CMHO-integrated CNN-LSTM model achieved a accuracy of 96.1%, outperforming traditional feature selection methods by 3%-5% (p < 0.05). The framework demonstrated stability and clinical interpretability by selecting validated biomarkers-including thalassemia, chest pain type, and maximum heart rate-with a Stability Selection Index (SSI) > 0.90.The CMHO framework provides a robust and interpretable tool for cardiovascular risk assessment. By navigating high-dimensional data across diverse populations, it offers a reliable computational approach for clinical decision support in cardiology.