Hybrid Deep Learning Framework for Continuous Blood Glucose Monitoring and Gestational Diabetes Risk Prediction.

Kiran Kumar Chanumolu; Muni nagamani G; Muni Hemalatha G

doi:10.2174/0115733998380389251111041618

January 9, 2026

Hybrid Deep Learning Framework for Continuous Blood Glucose Monitoring and Gestational Diabetes Risk Prediction.

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Key Points

To evaluate a hybrid framework for continuous glucose monitoring and predicting gestational diabetes risk.
Developed a hybrid deep learning model for glucose monitoring.
Applied the model for gestational diabetes risk assessment.
Established reliability metrics for clinical applicability.
Demonstrated high reliability in glucose monitoring.
Achieved accurate predictions for gestational diabetes risk.

Implication

A hybrid framework predicts blood glucose and gestational diabetes risk, suggesting clinical application for maternal care.

Abstract

The hybrid framework proposed here is reliable for clinically interpretable continuous glucose monitoring and GDM risk predictions. Its application with high reliability can lead to integrating it within clinical protocols for real-time maternal care sets.

Discussion

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Cite This Study

Chanumolu et al. (2026) studied this question.

synapsesocial.com/papers/69746126bb9d90c67120b0c6 https://doi.org/10.2174/0115733998380389251111041618

View at Publisher

Ask AI

Bookmark

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Hybrid Deep Learning Framework for Continuous Blood Glucose Monitoring and Gestational Diabetes Risk Prediction.

Key Points

Implication

Abstract

Discussion

Cite This Study

Hybrid Deep Learning Framework for Continuous Blood Glucose Monitoring and Gestational Diabetes Risk Prediction.

Key Points

Implication

Abstract

Discussion

Cite This Study

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