Abstract: Gestational diabetes mellitus (GDM), a condition occurring solely during pregnancy, poses risks to both expectant mothers and their infants, particularly among individuals with pre-existing risk factors. However, early diagnosis and effective management of GDM can help mitigate potential complications. As part of the Ministry of Health's efforts to enhance screening and management strategies for GDM in Malaysia, this study aims utilizing a rule-based technique, acting as an Expert System for Initial Screening of Gestational Diabetes Mellitus Detection. This application will facilitate early diagnosis by assessing risk factors and symptoms to calculate the probability of GDM occurrence and classify it as low, medium, or high. Functionality and usability tests are conducted to ensure error-free performance and gather user feedback. The study's findings indicate that the self-check GDM system effectively utilizes the algorithm, while the mobile application showcases good usability, achieving an above-average System Usability Scale (SUS) score.

Ayunnie Azmi, Nurulhuda Zainuddin, Azmi Aminordin and Masurah Mohamad, “GDM-PREP: A Rule-Based Technique to Enhance Early Detection of Gestational Diabetes Mellitus” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140786

@article{Azmi2023,
title = {GDM-PREP: A Rule-Based Technique to Enhance Early Detection of Gestational Diabetes Mellitus},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140786},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140786},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Ayunnie Azmi and Nurulhuda Zainuddin and Azmi Aminordin and Masurah Mohamad}
}