title: Improving prediction of preterm birth using a new classification scheme and rule induction.
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%).
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence 66045.
authors.forename: J W
authors.initials: JW
authors.lastname: Grzymala-Busse
authors.forename: L K
authors.initials: LK
authors.lastname: Woolery
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification / Q000379:methods*; D016208:Databases, Factual; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature; D008297:Male; D007752:Obstetric Labor, Premature; D011247:Pregnancy; D018570:Risk Assessment; D012984:Software