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Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification
Farida Mohsen, Md. Rafiul Biswas, Hazrat Ali, Tanvir Alam, Mowafa Househ, Zubair Shah
517 - 520
10.3233/SHTI220779
Research Article
Studies in Health Technology and Informatics
Volume 295: Advances in Informatics, Management and Technology in Healthcare
Abstract

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.

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