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Improving prediction of preterm birth using a new classification scheme and rule induction
Grzymala-Busse, Jerzy W. Woolery, Linda K.
Grzymala-Busse, Jerzy W.
Woolery, Linda K.
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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%).
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Date
1994
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HANLEY & BELFUS INC
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c38-amia.pdf
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Keywords
Information science, Library science, Information systems, Computer science, Interdisciplinary applications, Medical informatics
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.