Design and Development of Meal Intake Prediction Model for Gestational Diabetes Mellitus Patients using Genetic Algorithm

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

Abstract


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 model has successfully predicted the next meal intake according to the patient blood glucose levels. We hope this study will encourage research on development of self-monitoring applications to improve blood glucose control for GDM.

Keywords


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



DOI: http://doi.org/10.11591/ijai.v9.i4.pp%25p
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