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*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Informatics, Management and Technology in Healthcare
EditorsJohn Mantas, Parisis Gallos, Emmanouil Zoulias, Arie Hasman, Mowafa S. Househ, Marianna Diomidous, Joseph Liaskos, Martha Charalampidou
PublisherIOS Press BV
Pages517-520
Number of pages4
ISBN (Electronic)9781643682907
DOIs
Publication statusPublished - 2022

Publication series

NameStudies in Health Technology and Informatics
Volume295
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Keywords

  • AutoML
  • Diabetes classification
  • Ensemble model
  • Machine learning

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