Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89478
Title: A comprehensive exploration to the machine learning techniques for diabetes identification
Authors: Wei, Sidong
Zhao, Xuejiao
Miao, Chunyan
Keywords: Deep Neural Network
DRNTU::Engineering::Computer science and engineering
Machine Learning
Issue Date: 2018
Source: Wei, S., Zhao, X., & Miao, C. (2018). A comprehensive exploration to the machine learning techniques for diabetes identification. 2018 IEEE 4th World Forum on Internet of Things (WF-IoT). doi:10.1109/WF-IoT.2018.8355130
Abstract: Diabetes mellitus, known as diabetes, is a group of metabolic disorders and has affected hundreds of millions of people. The detection of diabetes is of great importance, concerning its severe complications. There have been plenty of research studies about diabetes identification, many of which are based on the Pima Indian diabetes data set. It’s a data set studying women in Pima Indian population started from 1965, where the onset rate for diabetes is comparatively high. Most of the research studies done before mainly focused on one or two particular complex technique to test the data, while a comprehensive research over many common techniques is missing. In this paper, we make a comprehensive exploration to the most popular techniques (e.g. DNN (Deep Neural Network), SVM (Support Vector Machine), etc.) used to identify diabetes and data preprocessing methods. Basically, we examine these techniques by the accuracy of cross-validation on the Pima Indian data set. We compare the accuracy of each classifier over several ways of data preprocessors and we modify the parameters to improve their accuracy. The best technique we find has 77.86% accuracy using 10-fold cross-validation. We also analyze the relevance between each feature with the classification result.
URI: https://hdl.handle.net/10356/89478
http://hdl.handle.net/10220/47703
DOI: 10.1109/WF-IoT.2018.8355130
Rights: © 2018 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved. This paper was published in 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) and is made available with permission of Institute of Electrical and Electronics Engineers (IEEE).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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