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September 10, 2025International Research Journal of Multidisciplinary TechnovationOpen Access

Enhancing Diabetes Diagnosis with Confidence-Calibrated Adaptive Weighting and Multi-Model Ensemble Framework

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Authors

VUVudatha UshaVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology RRRajalakshmi N.. R.Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

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Implication

This analysis demonstrates enhanced prediction accuracy for diabetes in diverse datasets, suggesting robust machine learning methods improve diagnostic reliability.

Key Points

The predictive model achieved an impressive accuracy of 98.9% on the Frankfurt dataset, indicating significant improvement in diabetes diagnosis.
Using K-fold cross-validation ensures robustness, validating the effectiveness of this machine learning approach across various data splits.
Incorporating multiple classifiers like Random Forest and XGBoost, complemented by adaptive weighting, strengthens prediction outcomes.
The Confidence-Calibrated Adaptive Weighting method boosts model influence based on accuracy and confidence, enhancing diagnostic stability.

Cite This Study

Usha et al. (2025) studied this question.

synapsesocial.com/papers/68c1a78154b1d3bfb60e106b https://doi.org/10.54392/irjmt25415

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Implication

This analysis demonstrates enhanced prediction accuracy for diabetes in diverse datasets, suggesting robust machine learning methods improve diagnostic reliability.

Enhancing Diabetes Diagnosis with Confidence-Calibrated Adaptive Weighting and Multi-Model Ensemble Framework

Discussion

Member takes

Implication

Key Points

Cite This Study

Enhancing Diabetes Diagnosis with Confidence-Calibrated Adaptive Weighting and Multi-Model Ensemble Framework

Discussion

Member takes

Implication

Key Points

Cite This Study

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