| 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. |