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    Improving prediction of preterm birth using a new classification scheme and rule induction

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    Issue Date
    1994
    Author
    Grzymala-Busse, Jerzy W.
    Woolery, Linda K.
    Publisher
    HANLEY & BELFUS INC
    Format
    22772 bytes
    Type
    Article
    Published Version
    https://academic.oup.com/jamia/issue
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    Abstract
    Prediction of preterm birth is a poorly understood domain. The existing manual methods of assessment of preterm birth are 17% - 38% accurate. The machine learning system LERS was used for three different datasets about pregnant women. Rules induced by LERS were used in conjunction with a classification scheme of LERS, based on ''bucket brigade algorithm'' of genetic algorithms and enhanced by partial matching. The resulting prediction of preterm birth in new, unseen cases is much more accurate (68%-90%).
    URI
    http://hdl.handle.net/1808/415
    Collections
    • Electrical Engineering and Computer Science Scholarly Works [301]
    Citation
    GRZYMALABUSSE, JW; WOOLERY, LK. Improving Prediction of Preterm Birth Using a New Classification Scheme and Rule Induction. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION. 1994. 730-734.

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    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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