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Classification tree analysis of postal questionnaire data to identify risk of excessive gestational weight gain

journal contribution

posted on 2016-01-01, 00:00 authored by Matthew Fuller-TyszkiewiczMatthew Fuller-Tyszkiewicz, Helen Skouteris, Briony Hill, H Teede, Skye Mcphie

Classification tree analysis of postal questionnaire data to identify risk of excessive gestational weight gain

History

Journal

Midwifery

Volume

32

Pagination

38 - 44

Publisher

ELSEVIER SCI LTD

Location

Scotland

Publisher DOI

https://doi.org/10.1016/j.midw.2015.10.007

ISSN

0266-6138

eISSN

1532-3099

Language

English

Author URL

http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000366010300006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a045e4b2bb1f2b747c68c720ec8913b7

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2016, Elsevier

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Keywords

Science & Technology Life Sciences & Biomedicine Nursing Gestational weight gain Pregnancy BMI Classification and regression tree analysis Psychosocial risk factors SLEEP QUALITY INDEX BODY-MASS INDEX POSTNATAL DEPRESSION WOMEN HEALTH MODEL SYMPTOMS OBESITY INTERVENTIONS

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All Rights Reserved

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