DI CVD TRI Layer CX Classifier for Secure IoT Enabled Risk Prediction Model
Thumilvannan S
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha University, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha University, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
This paper introduces a novel Di-CVD Tri-Layer CX Classifier, an IoT-integrated and machine learning (ML)-driven framework, to predict the individual and joint risk of diabetes (DB) and heart disease (HD). The proposed model comprises three phases: secure IoT-based data collection using Enhanced BGV encryption with Dynamic Distributed Hashing (DDH); a feature extraction (FE) phase leveraging (IGO) Information Gain Ratio and disease-specific ranking and a three-step classifier—Cm-Ro (FS) feature selection, hierarchical XGBoost classification, and synergistic prioritized risk scoring. By integrating multi-attribute features, rule-free optimization, and enhanced interoperability, the model addresses critical challenges such as heterogeneous data formats, poor feature relevance, and low interoperability in previous studies. When compared to conventional classifiers such as SVM and standard XGBoost, experimental evaluation on the NHANES dataset shows improved performance in terms of accuracy (ACC), recall (R), precision (P), and F1-score. The outcomes validate the framework’s effectiveness in early, secure, and individualized risk prediction, offering substantial support for timely interventions and enhanced patient care.
Keywords
Diabetes and Heart Disease Prediction, IoT-Integrated Healthcare, Machine Learning Classifier, Feature Extraction and Selection, Encrypted Health Data Processing.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Thumilvannan S and Balamanigandan R;
Methodology: Thumilvannan S;
Writing- Original Draft Preparation: Thumilvannan S and Balamanigandan R;
Visualization: Balamanigandan R;
Investigation: Thumilvannan S and Balamanigandan R;
Supervision: Thumilvannan S;
Validation: Balamanigandan R;
Writing- Reviewing and Editing: Thumilvannan S and Balamanigandan R; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr. Balamanigandan R for this research completion and support.
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Balamanigandan R
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha University, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
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Cite this article
Thumilvannan S and Balamanigandan R, “DI CVD TRI Layer CX Classifier for Secure IoT Enabled Risk Prediction Model”, Journal of Machine and Computing, vol.5, no.3, pp. 1571-1580, July 2025, doi: 10.53759/7669/jmc202505124.
