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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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 |
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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 |
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