IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 9, No 4: December 2020

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

Marshima Mohd Rosli (Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA)
Nor Shahida Mohamad Yusop (Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA)
Aini Sofea Fazuly (Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA)



Article Info

Publish Date
01 Dec 2020

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 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.

Copyrights © 2020






Journal Info

Abbrev

IJAI

Publisher

Subject

Computer Science & IT Engineering

Description

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...