Title: Classification techniques for diagnosis of diabetes: a review

Authors: Dilip Kumar Choubey; Sanchita Paul

Addresses: Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India ' Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India

Abstract: Classification is an efficient and most widely used technique in various applications, such as medical diagnosis of diabetes patients. There are various techniques implemented for the classification of diabetes patients, such as using supervised learning approach of Support Vector Machine (SVM). This technique provides not only the high accuracy of classification but also the high true positive rate when applied on some popular diabetes datasets, such as Pima Indian Diabetes Dataset. This paper summarises and compares various techniques that are implemented for the classification of medical diabetes diagnosis on various datasets. The techniques are analysed and compared on the basis of their advantages, issues, classification accuracy. So that on the basis of their issues a new and efficient technique for the classification of diabetes patients can be implemented.

Keywords: classification; Pima Indian diabetes dataset; medical diagnosis; feature selection; support vector machines; SVM; artificial neural networks; ANNs; diabetes diagnosis; WEKA; MATLAB; Java; Oracle; SPSS; soft computing; diabetics.

DOI: 10.1504/IJBET.2016.076730

International Journal of Biomedical Engineering and Technology, 2016 Vol.21 No.1, pp.15 - 39

Received: 24 Jun 2015
Accepted: 23 Sep 2015
Published online: 24 May 2016 *

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