Enhanced Model for Gestational Diabetes Mellitus Prediction Using a Fusion Technique of Multiple Algorithms with Explainability

Abstract High glucose levels during pregnancy cause Gestational Diabetes Mellitus (GDM). The risks include cesarean deliveries, long-term type 2 diabetes, fetal macrosomia, and infant respiratory distress syndrome. These risks highlight the need for accurate GDM prediction. This research proposes a novel fusion model for early GDM prediction. It uses conventional Machine Learning (ML) and advanced Deep Learning (DL) algorithms. Subsequently, it combines the strengths of both ML and DL algorithms using various ensemble techniques. It incorporates a meta-classifier that further reinforces its robust prediction performance. The dataset is split into training and testing sets in a 70/30 ratio. The initial steps involve exploratory analysis and data preprocessing techniques such as iterative imputation and feature engineering. Subsequently, oversampling is applied to the training set to address class imbalance which ensures the model learns effectively. The testing set remains imbalanced to maintain the credibility of the model’s performance evaluation. The fusion model achieves an accuracy of 98.21%, precision of 97.72%, specificity of 98.64%, recall of 97.47%, F1 score of 97.59%, and an Accuracy Under the Curve (AUC) of 99.91%. The model exhibits efficiency with an average processing time of 0.06 s to predict GDM. These results outperform the previous studies using the same GDM prediction dataset and demonstrate the model's superior performance. Additionally, Explainable Artificial Intelligence (XAI) techniques are utilized to interpret the model’s decisions. They highlight the most influential features in GDM prediction and ensures transparency. The proposed fusion model can facilitate proactive GDM prediction to elevate GDM management and maternal–fetal health outcomes.