A Hybrid Machine Learning Model for Enhanced Prediction of Gestational Diabetes Using Diverse Datasets

Abstract

Gestational diabetes mellitus (GDM) is a significant health concern affecting maternal and fetal well-being, necessitating early and accurate predictive models. This study presents a novel hybrid machine learning model integrating Random Forest, Support Vector Machine, and Gradient Boosting Machine through a stacking ensemble approach. The hybrid model achieved superior performance across two datasets, with accuracy scores of 92.7% and 89.02%, significantly outperforming individual models. The integration of diverse data sources, including clinical, biochemical, and demographic variables, enhanced the model's robustness and generalizability. Metrics such as precision (91.5% and 86.05%), F1-Score (92.3% and 73.18%), and ROC-AUC (0.94 and 0.91) underscore the model's ability to balance precision and recall effectively. The study addresses key research gaps, including generalizability issues, data integration, and scalability. By incorporating hyperparameter tuning, model pruning, and quantization, the hybrid model is optimized for deployment in resource-constrained settings, demonstrating scalability and efficiency. Despite its promise, challenges such as the need for external validation across diverse populations and addressing biases in training data remain. Future research should focus on fairness-aware algorithms and longitudinal studies to ensure equitable healthcare outcomes. This hybrid model showcases its potential as a reliable tool for early GDM detection, enabling timely interventions and improving maternal and fetal health outcomes. Its integration into clinical workflows and adaptability across healthcare settings highlight its significance as a step forward in precision medicine.