Preterm birth risk assessed by a new method of classification using selective partial matching

  • Jerzy W. Grzymala-Busse
  • Linda K. Goodwin
  • Xiaohui Zhang
Communications 8A Learning and Knowledge Discovery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1609)


In the United States, 8–12% of all newborns are delivered preterm, i.e., before 37 weeks of gestation. Most existing methods to assess preterm birth are based on risk scoring. These methods are only between 17% and 38% predictive in determining preterm birth. Hence there is need for data mining and knowledge discovery in database for predicting birth outcomes in pregnant women. This paper presents a new approach to classification (diagnosis) using selective partial matching. It is shown that our approach is more stable and, in general, more accurate than the method used so far. Our other result shows that classification based on more specific rules is worse.


Preterm birth machine learning knowledge discovery in databases data mining rough set theory rule induction classification of examples system LERS 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Jerzy W. Grzymala-Busse
    • 1
  • Linda K. Goodwin
    • 2
  • Xiaohui Zhang
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrence
  2. 2.Department of Information Services and the School of NursingDuke UniversityDurham

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