Detecting Depression in Children Through Machine Learning Using CatBoost and Random Forest Models

Depression, a prevalent yet often overlooked mood disorder, poses significant challenges in pediatric mental health due to subtle and variable symptom presentations across developmental stages. This study explores the potential of machine learning models—CatBoost and Random Forest—to detect depression in children using tabular data from the National Health Interview Survey (NHIS) (2019–2023). By leveraging consistent survey questions, particularly the DEPFREQ_C variable indicating the frequency of sadness or depression, the models classified children reporting “Daily” as positive cases. The dataset was partitioned into 70-20-10 for training, validation, and testing sets. CatBoost and Random Forest were trained using distinct hyperparameter optimization methods, yielding maximum accuracies of 93.24% and 91.84%, respectively. Performance metrics, including precision, recall, and F1-score, highlight CatBoost’s ability to handle categorical data and survey-specific variables. Additionally, analyses of pre- and post-COVID-19 data revealed minimal impact on model performance. While these models should only complement clinical diagnostics rather than replace them, these findings highlight the potential of machine learning in early-stage detection of depression among children.