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%). 1