Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm

Ebru Pekel Özmen*, Tuncay Özcan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Diabetes mellitus is one of the most important public health problems affecting millions of people worldwide. An early and accurate diagnosis of diabetes mellitus has critical importance for the medical treatments of patients. In this study, first, artificial neural network (ANN) and classification and regression tree (CART)-based approaches are proposed for the diagnosis of diabetes. Hybrid ANN-GA and CART-GA approaches are then developed using a genetic algorithm (GA) to improve the classification accuracy of these approaches. Finally, the performances of the developed approaches are evaluated with a Pima Indian diabetes data set. Experimental results show that the developed hybrid CART-GA approach outperforms the ANN, CART, and ANN-GA approaches in terms of classification accuracy, and this approach provides an efficient methodology for diagnosis of diabetes mellitus.

Original languageEnglish
Pages (from-to)661-670
Number of pages10
JournalJournal of Forecasting
Volume39
Issue number4
DOIs
Publication statusPublished - 1 Jul 2020

Bibliographical note

Publisher Copyright:
© 2020 John Wiley & Sons, Ltd.

Keywords

  • artificial neural network
  • classification
  • classification and regression tree
  • diabetes
  • genetic algorithm

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