Prediction of depressive disorder using machine learning approaches: findings from the NHANES.

ABSTRACT:

Background

Depressive disorder, particularly major depressive disorder (MDD), significantly impact individuals and society. Traditional analysis methods often suffer from subjectivity and may not capture complex, non-linear relationships between risk factors. Machine learning (ML) offers a data-driven approach to predict and diagnose depression more accurately by analyzing large and complex datasets.

Methods

This study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2013-2014 to predict depression using six supervised ML models: Logistic Regression, Random Forest, Naive Bayes, Support Vector Machine (SVM), Extreme Gradient Boost (XGBoost), and Light Gradient Boosting Machine (LightGBM). Depression was assessed using the Patient Health Questio

SUBMITTER: Vu T 

PROVIDER: S-EPMC11834192 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature