English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51931/87076 (60%)
Visitors : 8476973      Online Users : 122
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107024


    Title: An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus
    Authors: Wang, K.J.;Adrian, A.-M.;Chen, K.-H.;Wang, K.-M.
    Keywords: Diabetes mellitus;Electromagnetism-like mechanism algorithm;Feature selection;Nearest-neighbor heuristic;Opposite sign test
    Date: 2015-02-10
    Issue Date: 2016-08-15
    Publisher: Academic Press
    Abstract: Recently, the use of artificial intelligence based data mining techniques for massive medical data classification and diagnosis has gained its popularity, whereas the effectiveness and efficiency by feature selection is worthy to further investigate. In this paper, we presents a novel method for feature selection with the use of opposite sign test (OST) as a local search for the electromagnetism-like mechanism (EM) algorithm, denoted as improved electromagnetism-like mechanism (IEM) algorithm. Nearest neighbor algorithm is served as a classifier for the wrapper method. The proposed IEM algorithm is compared with nine popular feature selection and classification methods. Forty-six datasets from the UCI repository and eight gene expression microarray datasets are collected for comprehensive evaluation. Non-parametric statistical tests are conducted to justify the performance of the methods in terms of classification accuracy and Kappa index. The results confirm that the proposed IEM method is superior to the common state-of-art methods. Furthermore, we apply IEM to predict the occurrence of Type 2 diabetes mellitus (DM) after a gestational DM. Our research helps identify the risk factors for this disease; accordingly accurate diagnosis and prognosis can be achieved to reduce the morbidity and mortality rate caused by DM.
    Relation: Journal of Biomedical Informatics 54, pp.220-229
    DOI: 10.1016/j.jbi.2015.02.001
    Appears in Collections:[企業管理學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML39View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback