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Optimization of Classification Techniques for Diabetes Diagnosis

Author(s):

Seeema Varmora , SHREE PANDIT NATHULALJI VYAS TECHNICAL CAMPUS, WADHWAN; Rushirajsinh Zala, SHREE PANDIT NATHULALJI VYAS TECHNICAL CAMPUS, WADHWAN

Keywords:

Diabetes, Principal Component analysis (PCA), Neural Network (NN). Machine Learning (ML)

Abstract

Diabetes is a common but serious chronic disease. Nearly 8% of Americans who are aged 65 and older(about 10.9 million)suffer from this deadly disease. Self-management of this disease is possible, yet the older population lack knowledge, have denial and often lack motivation to do so. The data mining techniques can be successfully used for the classification of patients suffering from diabetes. The classification can be done to find out categories such as not detected, initial stage, middle stage and advanced stage of diabetes. This study has undertaken only two class based classification of positive (diabetes detected) and negative (diabetes not detected) class. Most of the work related to machine learning in the domain of diabetes diagnosis is concentrated on the study of the Pima Indian Diabetes dataset in the UCI repository. data sets which is available as an open source. In this particular work different approaches have been proposed for the classification of subjects into two classes namely: Diabetic & Non-diabetic. Neural network as classifier and Principle component analysis as dimension reduction technique.

Other Details

Paper ID: IJSRDV3I31114
Published in: Volume : 3, Issue : 3
Publication Date: 01/06/2015
Page(s): 1659-1661

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