Improving prediction of preterm birth using a new classification scheme and rule induction
Grzymala-Busse, Jerzy W.
Woolery, L. K.
HANLEY & BELFUS INC
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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%).
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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