Characterizing the Socioenvironmental and Behavioral Profile of Individuals with OCD Using the PNS 2019 Database
DOI:
https://doi.org/10.5753/jidm.2026.5780Keywords:
Machine learning, Health informatics, Obsessive-Compulsive Disorder, Health databaseAbstract
The objective of this study is to characterize the profile of individuals diagnosed with Obsessive-Compulsive Disorder (OCD) in the Brazilian population, considering socioenvironmental and behavioral aspects. For this purpose, the 2019 National Health Survey (PNS) database is considered. Based on a knowledge discovery process, including conceptual modeling of the domain for conceptual selection of attributes, the Explainable Boosting Machine (EBM) and Decision Tree algorithms are applied, aiming to identify relevant attributes for the classification of OCD. The results indicate that both aspects improve the model's performance, reaching an average F1-score of 63% (59% for OCD = yes, and 66% for OCD=No). Results consistent with the literature were also found, such as the relationship between OCD and poor sleep quality, diet quality, and mental disorders such as anxiety and depression, among other factors. This study has limitations, such as the use of data that may not accurately reflect socioeconomic and behavioral conditions during the development of OCD. Thus, this study serves as an exploratory guide, capable of identifying profiles more vulnerable to triggers of the disorder, but without the intention of replacing medical or psychological evaluation.
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