Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification.

This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.