Identifying Depression Using Machine Learning
DOI:
https://doi.org/10.54097/xsq2d171Keywords:
Depression, Machine Learning, CatBoost, Random Forest, Decision TreeAbstract
Depression is the leading cause of disability worldwide. However, accurately estimating the epidemiological factors that contribute to depression remains challenging. Deep learning algorithms can be used to assess the factors that contribute to the prevalence and clinical manifestations of depression. In this paper, five machine learning models, logistic regression, decision tree, random forest, SVM, and CatBoost, were used to assess depression in 5533 adult participants from the 2018 NHANES database. The results show that random forest is the best model for identifying depression, with the highest area under the working characteristic curve (AUC), followed by CatBoost and decision tree models. This suggests that using machine learning can accurately predict depression risk.
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