A Hybrid CNN-LSTM Framework for Pre-Diabetes Risk Prediction Using Longitudinal Health Data

Authors

  • Susmitha Cherukuri, Prasanna Laxmi Sama Author

DOI:

https://doi.org/10.48047/n86gyg25

Keywords:

Pre-Diabetes Prediction, Hybrid CNN-LSTM, Longitudinal Health Data, Deep Learning, Temporal Sequence Modelling, Diabetes Risk Assessment, Clinical Data Analytics, Preventive Healthcare.

Abstract

Pre-diabetes represents an intermediate metabolic state in which glucose regulation becomes impaired, increasing the likelihood of progression to type 2 diabetes and associated cardiovascular complications. Its development is influenced by multiple interacting factors, including age, body mass index, family history, dietary habits, physical inactivity

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Published

09.10.2026

Issue

Vol. 16 No. 2 (2026): volume 16 issue 2(2026)

Section

Articles

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How to Cite

A Hybrid CNN-LSTM Framework for Pre-Diabetes Risk Prediction Using Longitudinal Health Data. (2026). International Journal of Information and Electronics Engineering, 16(2), 721-732. https://doi.org/10.48047/n86gyg25