淡江大學機構典藏:Item 987654321/98680
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62797/95867 (66%)
Visitors : 3746329      Online Users : 596
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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/98680


    Title: Cluster-based Classification of Diabetic Nephropathy among Type 2
    Authors: Guan-Mau Huang;Yu-Chun Lee;Julia Tzu-Ya Weng;Yi-Cheng Chen;Lawrence Shih-Hsin Wu
    Contributors: 資訊工程學系暨研究所
    Keywords: type 2 diabetes
    diabetic nephropathy
    classification
    clustering
    genetic
    Date: 2014-05-07
    Issue Date: 2014-09-10 01:46:22 (UTC+8)
    Abstract: The prevalence of type 2 diabetes is increasing at an alarming rate. Various complications are associated with type 2 diabetes, with diabetic nephropathy being the leading cause of renal failure among diabetics. Often, when patients are diagnosed with diabetic nephropathy, their
    renal functions have already been significantly damaged, speeding up the progression towards end stage renal disease. Therefore, a risk prediction tool may be beneficial for the implementation of early treatment and prevention. In the present study, we propose to develop a prediction model integrating clustering and classification approaches for the
    identification of diabetic nephropathy among type 2 diabetes patients. Clinical and
    genotyping data are obtained from 345 type 2 diabetic patients(160 with non-diabetic
    nephropathy and 185 with diabetic nephropathy). The performance of using clinical features alone for cluster-based classification is compared with that of utilizing a combination of clinical and genetic attributes. We find that the inclusion of genetic features yield better
    prediction results. Further refinement of the proposed approach has the potential to facilitate the accurate identification of diabetic nephropathy and the development of better treatment in a clinical setting.
    Relation: The 3rd International Congress on Natural Sciences and Engineering (ICNSE'14), pp. 861-867.
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

    Files in This Item:

    File Description SizeFormat
    ICNSE'14- cluster-based classification of diabetic nephropathy among type 2 diabetic patients.pdf361KbAdobe PDF556View/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