Improving prediction of preterm birth using a new classification scheme and rule induction.

J W Grzymala-Busse; L K Woolery

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PubMed · 7950021

Improving prediction of preterm birth using a new classification scheme and rule induction.

J W Grzymala-Busse·L K Woolery

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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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Algorithms Artificial Intelligence Classification Databases, Factual Decision Support Techniques Female Humans Infant, Newborn Infant, Premature Male Obstetric Labor, Premature Pregnancy Risk Assessment Software Journal Article

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J W Grzymala-Busse, L K Woolery. 1994. Improving prediction of preterm birth using a new classification scheme and rule induction.. https://pubmed.ncbi.nlm.nih.gov/7950021/

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