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

Pekel Ozmen E., Özcan T.

JOURNAL OF FORECASTING, cilt.39, sa.4, ss.661-670, 2020 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 4
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1002/for.2652
  • Dergi Adı: JOURNAL OF FORECASTING
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, ABI/INFORM, Business Source Elite, Business Source Premier, Compendex, EconLit, INSPEC, Public Affairs Index, Sociological abstracts, zbMATH
  • Sayfa Sayıları: ss.661-670
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.