@inproceedings{ba54618efe1c49cc9dcfeaf0c6e344fc,
title = "Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification",
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.",
keywords = "AutoML, Diabetes classification, Ensemble model, Machine learning",
author = "Farida Mohsen and Biswas, {Md Rafiul} and Hazrat Ali and Tanvir Alam and Mowafa Househ and Zubair Shah",
note = "Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.",
year = "2022",
doi = "10.3233/SHTI220779",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "517--520",
editor = "John Mantas and Parisis Gallos and Emmanouil Zoulias and Arie Hasman and Househ, {Mowafa S.} and Marianna Diomidous and Joseph Liaskos and Martha Charalampidou",
booktitle = "Advances in Informatics, Management and Technology in Healthcare",
address = "Netherlands",
}