Published online Jul 19, 2022. doi: 10.5498/wjp.v12.i7.915
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
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.
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.
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.
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.
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.
Periodically checking on the emotional state of adolescents is required, along with providing individual counseling and conducting in-depth psychological examinations when necessary.
Longitudinal studies based on clinical depressive disorder data targeting depressive disorder in the high-risk group confirmed in this study should be conducted.