Cargando…
Factor Analysis of the Prediction of the Postpartum Depression Screening Scale
Postpartum depression (PPD), a severe form of clinical depression, is a serious social problem. Fortunately, most women with PPD are likely to recover if the symptoms are recognized and treated promptly. We designed two test data and six classifiers based on 586 questionnaires collected from a count...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950650/ https://www.ncbi.nlm.nih.gov/pubmed/31835547 http://dx.doi.org/10.3390/ijerph16245025 |
_version_ | 1783486120955215872 |
---|---|
author | Cai, Mei Wang, Yiming Luo, Qian Wei, Guo |
author_facet | Cai, Mei Wang, Yiming Luo, Qian Wei, Guo |
author_sort | Cai, Mei |
collection | PubMed |
description | Postpartum depression (PPD), a severe form of clinical depression, is a serious social problem. Fortunately, most women with PPD are likely to recover if the symptoms are recognized and treated promptly. We designed two test data and six classifiers based on 586 questionnaires collected from a county in North Carolina from 2002 to 2005. We used the C4.5 decision tree (DT) algorithm to form decision trees to predict the degree of PPD. Our study established the roles of attributes of the Postpartum Depression Screening Scale (PDSS), and devised the rules for classifying PPD using factor analysis based on the participants’ scores on the PDSS questionnaires. The six classifiers discard the use of PDSS Total and Short Total and make extensive use of demographic attributes contained in the PDSS questionnaires. Our research provided some insightful results. When using the short form to detect PPD, demographic information can be instructive. An analysis of the decision trees established the preferred sequence of attributes of the short form of PDSS. The most important attribute set was determined, which should make PPD prediction more efficient. Our research hopes to improve early recognition of PPD, especially when information or time is limited, and help mothers obtain timely professional medical diagnosis and follow-up treatments to minimize the harm to families and societies. |
format | Online Article Text |
id | pubmed-6950650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69506502020-01-16 Factor Analysis of the Prediction of the Postpartum Depression Screening Scale Cai, Mei Wang, Yiming Luo, Qian Wei, Guo Int J Environ Res Public Health Article Postpartum depression (PPD), a severe form of clinical depression, is a serious social problem. Fortunately, most women with PPD are likely to recover if the symptoms are recognized and treated promptly. We designed two test data and six classifiers based on 586 questionnaires collected from a county in North Carolina from 2002 to 2005. We used the C4.5 decision tree (DT) algorithm to form decision trees to predict the degree of PPD. Our study established the roles of attributes of the Postpartum Depression Screening Scale (PDSS), and devised the rules for classifying PPD using factor analysis based on the participants’ scores on the PDSS questionnaires. The six classifiers discard the use of PDSS Total and Short Total and make extensive use of demographic attributes contained in the PDSS questionnaires. Our research provided some insightful results. When using the short form to detect PPD, demographic information can be instructive. An analysis of the decision trees established the preferred sequence of attributes of the short form of PDSS. The most important attribute set was determined, which should make PPD prediction more efficient. Our research hopes to improve early recognition of PPD, especially when information or time is limited, and help mothers obtain timely professional medical diagnosis and follow-up treatments to minimize the harm to families and societies. MDPI 2019-12-10 2019-12 /pmc/articles/PMC6950650/ /pubmed/31835547 http://dx.doi.org/10.3390/ijerph16245025 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cai, Mei Wang, Yiming Luo, Qian Wei, Guo Factor Analysis of the Prediction of the Postpartum Depression Screening Scale |
title | Factor Analysis of the Prediction of the Postpartum Depression Screening Scale |
title_full | Factor Analysis of the Prediction of the Postpartum Depression Screening Scale |
title_fullStr | Factor Analysis of the Prediction of the Postpartum Depression Screening Scale |
title_full_unstemmed | Factor Analysis of the Prediction of the Postpartum Depression Screening Scale |
title_short | Factor Analysis of the Prediction of the Postpartum Depression Screening Scale |
title_sort | factor analysis of the prediction of the postpartum depression screening scale |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6950650/ https://www.ncbi.nlm.nih.gov/pubmed/31835547 http://dx.doi.org/10.3390/ijerph16245025 |
work_keys_str_mv | AT caimei factoranalysisofthepredictionofthepostpartumdepressionscreeningscale AT wangyiming factoranalysisofthepredictionofthepostpartumdepressionscreeningscale AT luoqian factoranalysisofthepredictionofthepostpartumdepressionscreeningscale AT weiguo factoranalysisofthepredictionofthepostpartumdepressionscreeningscale |