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Early Recognition of the Preference for Exclusive Breastfeeding in Current China: A Prediction Model based on Decision Trees
Exclusive breastfeeding (EBF) is affected by multiple risk factors. Therefore, it is difficult for clinical professionals to identify women who will not practice EBF well and provide subsequent medical suggestions and treatments. This study aimed to apply a decision tree (DT) model to predict EBF at...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7174406/ https://www.ncbi.nlm.nih.gov/pubmed/32317667 http://dx.doi.org/10.1038/s41598-020-63073-w |
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author | Wang, Yiting Shan, Chunjian Zhang, Yingying Ding, Lei Wen, Juan Tian, Yingying |
author_facet | Wang, Yiting Shan, Chunjian Zhang, Yingying Ding, Lei Wen, Juan Tian, Yingying |
author_sort | Wang, Yiting |
collection | PubMed |
description | Exclusive breastfeeding (EBF) is affected by multiple risk factors. Therefore, it is difficult for clinical professionals to identify women who will not practice EBF well and provide subsequent medical suggestions and treatments. This study aimed to apply a decision tree (DT) model to predict EBF at two months postpartum. The socio-demographic, clinical and breastfeeding parameters of 1,141 breastfeeding women from Nanjing were evaluated. Decision tree modelling was used to analyse and screen EBF factors and establish a risk assessment model of EBF. The Chinese version of the Breastfeeding Self-Efficacy Scale (CV-BSES) score, early formula supplementation, abnormal nipples, mastitis, neonatal jaundice, cracked or sore nipples and intended duration of breastfeeding were significant risk factors associated with EBF in the DT model. The accuracy, sensitivity and specificity of the DT model were 73.1%, 75.5% and 66.3%, respectively. The DT model showed similar or better performance than the logistic regression model in assessing the risk of early cessation of EBF before two months postpartum. The DT model has potential for application in clinical practice and identifies high-risk subpopulations that need specific prevention. |
format | Online Article Text |
id | pubmed-7174406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-71744062020-04-24 Early Recognition of the Preference for Exclusive Breastfeeding in Current China: A Prediction Model based on Decision Trees Wang, Yiting Shan, Chunjian Zhang, Yingying Ding, Lei Wen, Juan Tian, Yingying Sci Rep Article Exclusive breastfeeding (EBF) is affected by multiple risk factors. Therefore, it is difficult for clinical professionals to identify women who will not practice EBF well and provide subsequent medical suggestions and treatments. This study aimed to apply a decision tree (DT) model to predict EBF at two months postpartum. The socio-demographic, clinical and breastfeeding parameters of 1,141 breastfeeding women from Nanjing were evaluated. Decision tree modelling was used to analyse and screen EBF factors and establish a risk assessment model of EBF. The Chinese version of the Breastfeeding Self-Efficacy Scale (CV-BSES) score, early formula supplementation, abnormal nipples, mastitis, neonatal jaundice, cracked or sore nipples and intended duration of breastfeeding were significant risk factors associated with EBF in the DT model. The accuracy, sensitivity and specificity of the DT model were 73.1%, 75.5% and 66.3%, respectively. The DT model showed similar or better performance than the logistic regression model in assessing the risk of early cessation of EBF before two months postpartum. The DT model has potential for application in clinical practice and identifies high-risk subpopulations that need specific prevention. Nature Publishing Group UK 2020-04-21 /pmc/articles/PMC7174406/ /pubmed/32317667 http://dx.doi.org/10.1038/s41598-020-63073-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Wang, Yiting Shan, Chunjian Zhang, Yingying Ding, Lei Wen, Juan Tian, Yingying Early Recognition of the Preference for Exclusive Breastfeeding in Current China: A Prediction Model based on Decision Trees |
title | Early Recognition of the Preference for Exclusive Breastfeeding in Current China: A Prediction Model based on Decision Trees |
title_full | Early Recognition of the Preference for Exclusive Breastfeeding in Current China: A Prediction Model based on Decision Trees |
title_fullStr | Early Recognition of the Preference for Exclusive Breastfeeding in Current China: A Prediction Model based on Decision Trees |
title_full_unstemmed | Early Recognition of the Preference for Exclusive Breastfeeding in Current China: A Prediction Model based on Decision Trees |
title_short | Early Recognition of the Preference for Exclusive Breastfeeding in Current China: A Prediction Model based on Decision Trees |
title_sort | early recognition of the preference for exclusive breastfeeding in current china: a prediction model based on decision trees |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7174406/ https://www.ncbi.nlm.nih.gov/pubmed/32317667 http://dx.doi.org/10.1038/s41598-020-63073-w |
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