Design of meal intake prediction for gestational diabetes mellitus using genetic algorithm

Marshima Mohd Rosli, Nor Shahida Mohamad Yusop, Aini Sofea Fazuly


Gestational diabetes mellitus (GDM) is frequently described as glucose intolerance for pregnancy women. GDM patients currently practice the traditional method (record book) for recording blood glucose readings and keeping track of meal intake. This practice is not efficient and impractical for monitoring glucose level for GDM patients when we compared with mobile health monitoring technologies available today. Although, many applications have been developed for diabetes patients, but we do not found any application appropriate for GDM monitoring. In this study, we describe the design and development of mobile application for GDM monitoring using genetic algorithm that aims to predict recommended meal intake. We developed the mobile application for the GDM patients to maintain their blood glucose level through their meals. We tested the components of the mobile application and found that the prediction algorithm has successfully predicted the next meal intake according to the patient blood glucose levels. We hope this study will encourage research on development of selfmonitoring applications to improve blood glucose control for GDM.


Blood glucose,, GDM, Gestational diabetes mellitus, Recommender system, Systematic mapping

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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