Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
378167 | Artificial Intelligence in Medicine | 2006 | 15 Pages |
Abstract
The present study demonstrated that classification trees can be used to identify high-risk subgroups of mothers who are at risk of LBW outcomes. Although these exploratory tree analyses revealed a number of distinctive variable interactions for each geographical area, the variable selection was similar across all seven regions. This study also demonstrated that classification trees did not outperform logistic regression models or vice versa; both approaches provided useful analyses of the data.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Panagiota Kitsantas, Myles Hollander, Lei Li,