Cargando…

Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier

Machine learning is about finding patterns and making predictions from raw data. In this study, we aimed to achieve two goals by utilizing the modern logistic regression model as a statistical tool and classifier. First, we analyzed the associations between Major Depressive Episode with Severe Impai...

Descripción completa

Detalles Bibliográficos
Autores principales: Chiu, I.-Ming, Lu, Wenhua, Tian, Fangming, Hart, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870980/
https://www.ncbi.nlm.nih.gov/pubmed/33575244
http://dx.doi.org/10.3389/fpubh.2020.622007
_version_ 1783648920720637952
author Chiu, I.-Ming
Lu, Wenhua
Tian, Fangming
Hart, Daniel
author_facet Chiu, I.-Ming
Lu, Wenhua
Tian, Fangming
Hart, Daniel
author_sort Chiu, I.-Ming
collection PubMed
description Machine learning is about finding patterns and making predictions from raw data. In this study, we aimed to achieve two goals by utilizing the modern logistic regression model as a statistical tool and classifier. First, we analyzed the associations between Major Depressive Episode with Severe Impairment (MDESI) in adolescents with a list of broadly defined sociodemographic characteristics. Using findings from the logistic model, the second and ultimate goal was to identify the potential MDESI cases using a logistic model as a classifier (i.e., a predictive mechanism). Data on adolescents aged 12–17 years who participated in the National Survey on Drug Use and Health (NSDUH), 2011–2017, were pooled and analyzed. The logistic regression model revealed that compared with males and adolescents aged 12-13, females and those in the age groups of 14-15 and 16-17 had higher risk of MDESI. Blacks and Asians had lower risk of MDESI than Whites. Living in single-parent household, having less authoritative parents, having negative school experiences further increased adolescents' risk of having MDESI. The predictive model successfully identified 66% of the MDESI cases (recall rate) and accurately identified 72% of the MDESI and MDESI-free cases (accuracy rate) in the training data set. The rates of both recall and accuracy remained about the same (66 and 72%) using the test data. Results from this study confirmed that the logistic model, when used as a classifier, can identify potential cases of MDESI in adolescents with acceptable recall and reasonable accuracy rates. The algorithmic identification of adolescents at risk for depression may improve prevention and intervention.
format Online
Article
Text
id pubmed-7870980
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-78709802021-02-10 Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier Chiu, I.-Ming Lu, Wenhua Tian, Fangming Hart, Daniel Front Public Health Public Health Machine learning is about finding patterns and making predictions from raw data. In this study, we aimed to achieve two goals by utilizing the modern logistic regression model as a statistical tool and classifier. First, we analyzed the associations between Major Depressive Episode with Severe Impairment (MDESI) in adolescents with a list of broadly defined sociodemographic characteristics. Using findings from the logistic model, the second and ultimate goal was to identify the potential MDESI cases using a logistic model as a classifier (i.e., a predictive mechanism). Data on adolescents aged 12–17 years who participated in the National Survey on Drug Use and Health (NSDUH), 2011–2017, were pooled and analyzed. The logistic regression model revealed that compared with males and adolescents aged 12-13, females and those in the age groups of 14-15 and 16-17 had higher risk of MDESI. Blacks and Asians had lower risk of MDESI than Whites. Living in single-parent household, having less authoritative parents, having negative school experiences further increased adolescents' risk of having MDESI. The predictive model successfully identified 66% of the MDESI cases (recall rate) and accurately identified 72% of the MDESI and MDESI-free cases (accuracy rate) in the training data set. The rates of both recall and accuracy remained about the same (66 and 72%) using the test data. Results from this study confirmed that the logistic model, when used as a classifier, can identify potential cases of MDESI in adolescents with acceptable recall and reasonable accuracy rates. The algorithmic identification of adolescents at risk for depression may improve prevention and intervention. Frontiers Media S.A. 2021-01-26 /pmc/articles/PMC7870980/ /pubmed/33575244 http://dx.doi.org/10.3389/fpubh.2020.622007 Text en Copyright © 2021 Chiu, Lu, Tian and Hart. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Chiu, I.-Ming
Lu, Wenhua
Tian, Fangming
Hart, Daniel
Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier
title Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier
title_full Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier
title_fullStr Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier
title_full_unstemmed Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier
title_short Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier
title_sort early detection of severe functional impairment among adolescents with major depression using logistic classifier
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870980/
https://www.ncbi.nlm.nih.gov/pubmed/33575244
http://dx.doi.org/10.3389/fpubh.2020.622007
work_keys_str_mv AT chiuiming earlydetectionofseverefunctionalimpairmentamongadolescentswithmajordepressionusinglogisticclassifier
AT luwenhua earlydetectionofseverefunctionalimpairmentamongadolescentswithmajordepressionusinglogisticclassifier
AT tianfangming earlydetectionofseverefunctionalimpairmentamongadolescentswithmajordepressionusinglogisticclassifier
AT hartdaniel earlydetectionofseverefunctionalimpairmentamongadolescentswithmajordepressionusinglogisticclassifier