Deprivation of Critical Care systems are a major cause of fatality worldwide, highlighting it’s need for saving human lives. This study proposes a novel hybrid ensemble model, which integrates Random Forests, Gradient Boosting Machines (GBM), and Neural Networks to enhance the predictive accuracy diagnostics. The methodology combines data pre-processing, feature selection, and ensemble learning, ensuring robust and reliable predictions. Comprehensive data pre-processing includes K-Nearest Neighbours (KNN) imputation for missing values, Z-Score normalization for scaling, and Polynomial Feature Generation for non-linear feature interactions. Feature selection performed using Recursive Feature Elimination (RFE) and Mutual Information relevant variable retention. The proposed model produces 98.55% accuracy, very surpassing nine baseline models, that includes XGBoost, Random Forests, and Neural Networks. Additional metrics such as precision (97.80%), recall (98.12%), F1-Score (98.00%), and ROC-AUC (99.12%) further validate the model's robustness. This framework not only demonstrates superior accuracy but also ensures computational efficiency, making it viable for deployment in real-world healthcare settings.
Keywords
Early Detection, AI-Powered Framework, Ensemble Learning, Random Forests, Gradient Boosting Machines, Neural Networks, Machine Learning, Predictive Model, Feature Selection.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Vishnu Priyan S, Vijayalakshmi N, Suresh G and Rajesh K;
Methodology: Vishnu Priyan S and Vijayalakshmi N;
Software: Suresh G and Rajesh K;
Data Curation: Vishnu Priyan S and Vijayalakshmi N;
Writing- Original Draft Preparation: Vishnu Priyan S, Vijayalakshmi N, Suresh G and Rajesh K;
Visualization: Vishnu Priyan S and Vijayalakshmi N;
Investigation: Suresh G and Rajesh K;
Supervision: Vishnu Priyan S and Vijayalakshmi N;
Validation: Suresh G and Rajesh K;
Writing- Reviewing and Editing: Vishnu Priyan S, Vijayalakshmi N, Suresh G and Rajesh K;
All authors reviewed the results and approved the final version of the manuscript.
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The authors would like to thank to the reviewers for nice comments on the manuscript.
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Vishnu Priyan S
Department of Biomedical Engineering, Kings Engineering College, Chennai, Tamil Nadu, India.
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Cite this article
Vishnu Priyan S, Vijayalakshmi N, Suresh G and Rajesh K, “Advancing Health Diagnostics: AI-Powered CVD-REF Framework for Precise and Early Risk Assessment”, Journal of Machine and Computing, pp. 1248-1264, April 2025, doi: 10.53759/7669/jmc202505098.
