Automatic Diagnosis of Psychiatric Diseases in Adolescents with Machine Learning Methods using DsmV and Various Scales
Abstract
Adolescence is a difficult time for both teenagers and their families, they will experience fewer collisions. Teenagers sometimes want to be left alone. During this time, young people need to be recognized and valued. Teenagers are sad and pessimistic. Above all, young people need to feel understood and valued. Otherwise, adolescents need another environment to satisfy these feelings. Adolescence is a difficult time in life and a psychologically difficult period for the individual and the family. Youth is an important factor in the development of a country in all areas. For this reason, puberty should be managed appropriately and a prompt diagnosis/treatment process should be applied if a psychiatric illness occurs. The diagnosis of mental illness is also based on expert observation and requires good expertise. Of course, these systems are decision support systems and the final decision is left to the experts. In this study, we used machine learning to research machine learning for the automated processing of mental illness during the difficult life stages of adolescence. The results obtained are very fruitful and promising in this field and will constitute an important resource for scientists. In our dataset, the TLC+RF machine learning model (accuracy: 82%, ROC area: 0.925, TP: 0.82, FP: 0.037) achieved high classification success rates at all scales, demonstrating that computer-assisted diagnostic systems can be used in the diagnosis of adolescent psychiatric disorders.
Keywords
Psychiatric illnesses in adolescents, Automatic diagnosis, Deep learning, DSMV
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