Key result
Hybrid Genetic-Red Kite algorithm outperforms standard methods by predicting CHD with up to ~98% accuracy.
Why the study?
Does a hybrid Genetic-Red Kite Optimization Algorithm improve the accuracy of Random Forest models for coronary heart disease prediction?
Does a hybrid Genetic-Red Kite Optimization Algorithm improve the accuracy of Random Forest models for coronary heart disease prediction?
A hybrid Genetic-Red Kite Optimization Algorithm for Random Forest models achieves high accuracy in predicting coronary heart disease while providing feature-level interpretability.
Answers are grounded in this paper and cite their sources.
May inform exploratory ML-based CHD prediction; leaves open prospective clinical validation before any practice consideration.
Coronary heart disease continues to be a major cause of death worldwide, and insufficient ongoing health monitoring makes it difficult to make precise predictions. Real-time patient data collection is made possible by modern IoT devices, but their massive data volumes necessitate efficient machine learning analysis. In order to optimize Random Forest hyperparameters for better CHD prediction, this study suggests a hybrid Genetic-Red Kite Optimization Algorithm. Explainable AI techniques are integrated to enhance model transparency, and feature-level interpretability. Experiments conducted on Framingham, Z-Alizadeh Sani, and a comprehensive combined dataset demonstrate accuracies of 91.5%, 97.7%, and 95.2%, outperforming state-of-the-art methods across multiple evaluation metrics under diverse clinical scenarios.
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Ramudu et al. (2026) studied Coronary heart disease. Hybrid Genetic-Red Kite Optimization Algorithm for Random Forest vs. State-of-the-art methods was evaluated on Prediction accuracy. A hybrid Genetic-Red Kite Optimization Algorithm for Random Forest achieved CHD prediction accuracies of 91.5%, 97.7%, and 95.2% across different datasets, outperforming state-of-the-art methods.
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