Byeon H. Predicting South Korea adolescents vulnerable to depressive disorder using Bayesian nomogram: A community-based cross-sectional study. World J Psychiatry 2022; 12(7): 915-928 [PMID: 36051598 DOI: 10.5498/wjp.v12.i7.915]
Corresponding Author of This Article
Haewon Byeon, DSc, PhD, Associate Professor, Director, Department of Medical Big Data, College of AI Convergence, Inje University, No. 329 C-hall (Shineo Hall), Gimhae 50834, Gyeonsangnamdo, South Korea. bhwpuma@naver.com
Research Domain of This Article
Psychiatry
Article-Type of This Article
Case Control Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Jul 19, 2022; 12(7): 915-928 Published online Jul 19, 2022. doi: 10.5498/wjp.v12.i7.915
Predicting South Korea adolescents vulnerable to depressive disorder using Bayesian nomogram: A community-based cross-sectional study
Haewon Byeon
Haewon Byeon, Department of Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, South Korea
Author contributions: Byeon H designed the study, interpreted the data, preformed the statistical analysis, and wrote the article.
Supported byBasic Science Research Program through the National Research Foundation of Korea (NRF) Funded by the Ministry of Education, No. NRF-2018R1D1A1B07041091 and No. NRF-2021S1A5A8062526.
Institutional review board statement: The study was approved by the Research Ethics Review Board of the National Youth Policy Institute (No. KCYPS-2018).
Informed consent statement: All patients gave informed consent prior to study participation.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: Technical appendix, statistical code from the corresponding author at bhwpuma@naver.com.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Haewon Byeon, DSc, PhD, Associate Professor, Director, Department of Medical Big Data, College of AI Convergence, Inje University, No. 329 C-hall (Shineo Hall), Gimhae 50834, Gyeonsangnamdo, South Korea. bhwpuma@naver.com
Received: February 6, 2022 Peer-review started: February 6, 2022 First decision: April 18, 2022 Revised: April 20, 2022 Accepted: June 22, 2022 Article in press: June 22, 2022 Published online: July 19, 2022 Processing time: 163 Days and 1.7 Hours
ARTICLE HIGHLIGHTS
Research background
Although South Korea has developed and carried out evidence-based intervention and prevention programs to mitigate depressive disorder in adolescents, the number of adolescents with depressive disorder has increased every year for the past 10 years. Consequently, it is necessary to identify the influential factors causing depression and develop a predictive model with high accuracy that can identify groups highly vulnerable to depressive disorder as soon as possible.
Research motivation
Recently, the naïve Bayesian nomogram has been used as a method of predicting groups at high risk of developing diseases. One of the advantages of this method is that it presents the risk probability according to multiple risk factors of a disease visually so that clinicians can easily understand the results.
Research objectives
In this study, a nomogram based on a naïve Bayesian algorithm using epidemiological data on adolescents in South Korea was developed and baseline data for screening depressive disorder in adolescents was presented.
Research methods
We used a naïve Bayes classifier as the algorithm to develop the nomogram. Also, we calculated the general accuracy, precision, recall, F-1 score, the area under the curve, and calibration plot using leave-one-out cross-validation of the developed Bayesian algorithm-based nomogram to validate its predictive performance.
Research results
We showed that physical symptoms, aggression, social withdrawal, attention, satisfaction with school life, mean sleeping hours, and conversation time with parents were influential factors associated with depressive disorder in adolescents.
Research conclusions
Periodically checking on the emotional state of adolescents is required, along with providing individual counseling and conducting in-depth psychological examinations when necessary.
Research perspectives
Longitudinal studies based on clinical depressive disorder data targeting depressive disorder in the high-risk group confirmed in this study should be conducted.
