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

Bibliographic Data

ID22067751
AuthorsI-Ming Chiu (Rutgers, the State University of New Jersey), Wenhua Lu (0000-0002-6299-3779, City University of New York, corresponding author), Fangming Tian (0009-0000-5592-0320, Rutgers, the State University of New Jersey), Daniel Hart (0009-0007-1207-7629, Rutgers, the State University of New Jersey)
Year2021
Volume8
Pages622007-622007
Publication date2021-01-26
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2020.622007
PMID33575244
OpenAlexW3121599936
LanguageEN
References cited12

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

Logistic regression · Machine learning · Raw data · Recall · Statistics · Child and Adolescent Psychosocial and Emotional Development · Computer Science · Demography · Maternal Mental Health During Pregnancy and Postpartum · Mathematics · Medicine · Mental Health Research Topics · Psychology · Artificial Intelligence · Internal Medicine