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All Journal IAES International Journal of Robotics and Automation (IJRA)
Sathishkumar Krishnaveni
RVS Technical Campus
Automotive Engineering Electrical & Electronics Engineering

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DIETARY-GDM: AI-powered dual-source framework for personalized dietary recommendation in gestational diabetes management Karthick Myilvahanan Jothivel; Arun Madhan; Sathishkumar Krishnaveni; Suganya Arumugam; Kamalakannan Subbiah; Dharmaraj Thalaihatty Belli
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp709-719

Abstract

Gestational diabetes mellitus (GDM) is a condition of glucose intolerance that occurs during pregnancy, characterized by elevated blood glucose levels. However, the existing approach was validated on limited datasets, which may affect its generalizability to diverse real-world clinical settings, and its real-time deployment feasibility has not been thoroughly examined. In this research, a novel DIETARY-GDM (DIETARY food recommendation for GDM) has been proposed for managing diabetes in pregnant women through personalized diet food recommendations. Wavelet denoising and text cleaning are employed to remove noise in the signal and user input to enhance the quality. The enhanced signals and text are then extracted using the convolutional neural network-long short-term memory (CNN-LSTM) and universal sentence encoding (USE) to effectively analyze the body condition of the pregnant women. A deep neural network (DNN) integrated with a Bayesian optimization algorithm (BOA) is utilized to predict personalized dietary food recommendations. Finally, an artificial intelligence (AI)-BOT generates food suggestions for pregnant women to improve maternal health. From the experimental results, the DIETARY-GDM achieves an accuracy of 99.21%. The accuracy of the DIETARY-GDM was higher than that of the convolutional neural network (CNN), artificial neural network (ANN), gated recurrent unit (GRU), and long short-term memory (LSTM) by 7.98%, 5.65%, 3.35%, and 1.97%, respectively.
Co-Authors Arun Madhan Dharmaraj Thalaihatty Belli Kamalakannan Subbiah Karthick Myilvahanan Jothivel Suganya Arumugam