A mapping study on blood glucose recommender system for patients with gestational diabetes mellitus

Shuhada Mohd Rosli, Marshima Mohd Rosli, Rosmawati Nordin


Blood glucose (BG) prediction system can help gestational diabetes mellitus (GDM) patient to improve the BG control with managing their dietary intake based on healthy food. Many techniques have been developed to deal with blood glucose prediction, especially those for recommender system. In this study, we conduct a systematic mapping study to investigate recent research about BG prediction in recommender systems. This study describes an overview of research (2014-2018) about BG prediction techniques that has been used for BG recommender system. As results, 25 studies concerning BG prediction in recommender system were selected. We observed that although there is numerous studies published, only a few studies took serious discussion about techniques used to incorporate the BG algorithms. Our result highlighted that only one study discusses hybrid filtering technique in BG recommender system for GDM even though it has an ability to learn from experience and to improve prediction performance. We hope that this study will encourage researchers to consider not only machine learning and artificial intelligent techniques but also hybrid filtering technique for BG recommender system in the future research.


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

Full Text:


DOI: https://doi.org/10.11591/eei.v8i4.1633


  • There are currently no refbacks.

Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).