Empowering Healthcare with AI: Early Detection and Explainable Diagnosis of GDM

Abstract

Gestational Diabetes Mellitus (GDM) is a type of diabetes that develops during pregnancy and can affect both the mother and the fetus. Screening and identification of GDM at an early stage is very vital to ensure that no complications take place. This thesis proposes a framework for the early prediction of GDM employing machine-learning techniques with particular adherence to model interpretability. The used methodology incorporates data preprocessing, feature selection, evaluation of the model, and explainability of the performed predictions. Thus, in synthesizing the study, the aspect of Explainable AI ‘XAI’ is incorporated, guaranteeing the model gives accurate and plausible predictions. In addition, our method demonstrates a better performance than previous works even when we remove some of the features, stressing the significance of the feature selection in increasing the effectiveness of the models. The explainability methods increase the trust of users in the model predictions because the information is made available to them hence improving their decision-making processes. It is in this regard that the following thesis lays down the groundwork towards attaining a robust and explainable system for early GDM identification. It applies in enhancing meternal care and the baby through early detection and intervention and enhancing decisions on healthcare.