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- W2160157029 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%)." @default.
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- W2160157029 date "1994-01-01" @default.
- W2160157029 modified "2023-10-12" @default.
- W2160157029 title "Improving prediction of preterm birth using a new classification scheme and rule induction." @default.
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