Comparison of different methods for determining diabetes

Authors

MEHMET RECEP BOZKURT
NİLÜFER YURTAY
ZİYNET YILMAZ
CENGİZ SERTKAYA

DOI

10.3906/elk-1209-82

Abstract

In this study, the Pima Indian Diabetes dataset was categorized with 8 different classifiers. The data were taken from the University of California Irvine Machine Learning Repository's web site. As a classifier, 6 different neural networks [probabilistic neural network (PNN), learning vector quantization, feedforward networks, cascade-forward networks, distributed time delay networks (DTDN), and time delay networks], the artificial immune system, and the Gini algorithm from decision trees were used. The classifier's performance ratios were studied separately as accuracy, sensitivity, and specificity and the success rates of all of the classifiers are presented. Among these 8 classifiers, the best accuracy and specificity values were achieved with the DTDN and the best sensitivity value was achieved with the PNN.

Keywords

Diabetes diagnosis, artificial neural networks, decision tree, artificial immune system, classification, Pima Indian

First Page

1044

Last Page

1055

Recommended Citation

BOZKURT, MEHMET RECEP; YURTAY, NİLÜFER; YILMAZ, ZİYNET; and SERTKAYA, CENGİZ (2014) "Comparison of different methods for determining diabetes," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 22: No. 4, Article 17. https://doi.org/10.3906/elk-1209-82
Available at: https://journals.tubitak.gov.tr/elektrik/vol22/iss4/17