The cascade integration model based on machine learning to predict gestational diabetes

Abstract Machine learning has significant advantages in the research environment of disease prediction due to its data analysis and exploration capabilities. In recent years, despite progress in identifying risk factors for gestational diabetes mellitus (GDM), however no predictive models have been developed in clinical practice to date. This work improves GDM predictive models by developing a new integrated learning model building approach to more fully utilise the benefits of machine learning models to bring the disease management port forward. Initially, the clinical data set is normalized. Then, according to the principle of removing the redundant features of each machine learning model, the first nine high-importance features of the five single models are filtered respectively. Finally, the GDM Cascade integration prediction model is constructed and compared with the Blending model and Stacking model, it is obvious that the proposed model construction method has superior performance and the AUC value reaches 0.9536.