Abstract
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.
This work was supported in part by the National Library of Medicine, USA, Grant No. RO1 LM-O6488-02 and by the Committee for Scientific Research, Warsaw, Poland, Grant No. 8T11C00512.
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© 1999 Springer-Verlag Berlin Heidelberg
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Grzymala-Busse, J.W., Goodwin, L.K., Zhang, X. (1999). Preterm birth risk assessed by a new method of classification using selective partial matching. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095150
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DOI: https://doi.org/10.1007/BFb0095150
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