dc.contributor.authorWei, Sidong
dc.contributor.authorZhao, Xuejiao
dc.contributor.authorMiao, Chunyan
dc.date.accessioned2019-02-19T06:34:52Z
dc.date.available2019-02-19T06:34:52Z
dc.date.issued2018
dc.identifier.citationWei, 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.2018.8355130en_US
dc.identifier.urihttp://hdl.handle.net/10220/47703
dc.description.abstractDiabetes 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.en_US
dc.format.extent5 p.en_US
dc.language.isoenen_US
dc.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).en_US
dc.subjectMachine Learningen_US
dc.subjectDeep Neural Networken_US
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleA comprehensive exploration to the machine learning techniques for diabetes identificationen_US
dc.typeConference Paper
dc.contributor.conference2018 IEEE 4th World Forum on Internet of Things (WF-IoT)en_US
dc.contributor.researchNTU-UBC Research Centre of Excellence in Active Living for the Elderlyen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/WF-IoT.2018.8355130
dc.description.versionAccepted versionen_US
dc.identifier.rims208286


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