Every year, it is projected that approximately 2% to 10% of the pregnant mothers suffer from gestational diabetes mellitus (GDM) globally. In Africa, the average prevalence of GDM is 13.6%, though slightly higher in the sub-Saharan regions. Women with GDM are more likely to suffer from complications such as prolonged labor, development of type 2 diabetes, operative delivery and pre-eclampsia while the infants are at risk of macrosomia as a result of the accelerated fetal growth due to neonatal hyperglycemia, preterm delivery and maternal hyperglycemia.

While there exist several studies that attempted to use ML algorithms for early prediction of GDM, it is worth noting that most of the studies were done in China with very few studies using data collected in other low and middle-income countries and no studies available on data collected in Kenya. The primary objective of this research is to create precise and resilient ML algorithms that can promptly predict Gestational Diabetes Mellitus (GDM) in Kenya. By achieving this, we aim to enhance maternal and fetal health outcomes through timely interventions. Additionally, the research findings will contribute to knowledge of GDM risk factors, potentially leading to further research and advancements in prenatal care.

Aims:

• To summarize GDM risk factors at first and second trimester of pregnancy via descriptive statistics for patients enrolled at Aga Khan University, Kenya for antenatal visits and delivery.
• To develop a robust ML algorithm for early prediction of GDM in the Kenyan setting.
• To validate the performance of the developed machine learning algorithm for early prediction of gestational diabetes mellitus.