AUTHOR=Chiu I.-Ming , Lu Wenhua , Tian Fangming , Hart Daniel 

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

JOURNAL=Frontiers in Public Health

VOLUME=Volume 8 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2020.622007

DOI=10.3389/fpubh.2020.622007

ISSN=2296-2565

ABSTRACT=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.