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Title Data mining for preterm birth prediction
Author(s) Linda Goodwin and Sean Maher
Source Proceedings of the 2000 ACM symposium on Applied Computing, Pages 46-51
ISBN 1-58113-240-9
Publication Date 2000
Abstract Accurate assessment of preterm birth risk remains difficult due to a complex and disorganized knowledge domain, data and information overload, and the absence of reliable and valid tools to measure and predict preterm birth risk. The most persistent limitation for preterm birth risk prediction is our continued lack of understanding about the causes of preterm birth. The purpose of this study was to develop tools and techniques to help better understand the causes of premature birth. Results found only small differences in performance between five different modeling techniques that used neural networks, logistic regression, CART, and software, called PVRuleMiner and FactMiner, specially developed for dealing with problems inherent in clinical data. Contrary to clinical wisdom and earlier studies, most of the predictive power in the database used for this study (1,233 variables total) was found in 32 demographic variables, with only very slight improvements in predictive accuracy when hundreds of variables were added to the models. The ultimate goal of this research is to provide decision support for perinatal care providers to accurately identify patients at risk and assist them with modifiable preterm birth risk factors.

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