Predictors of mental health literacy among adolescents using a random forest model: a machine learning approach

Sarbhan Singh^1*, Nuur Hafizah Md. Iderus¹, Sumarni Mohd Ghazali¹, Lonny Chen Rong Qi Ahmad¹, Nur'ain Mohd Ghazali¹, Mohd Nadzmi Md Nadzri¹, Asrul Anuar Zulkifli¹, Mohd Kamarulariffin Kamarudin¹, Teh Chien Huey¹, Chew Cheng Hoon¹, Lim Mei Cheng¹

1. Special Resource Centre, Institute for Medical Research (IMR), National Institutes of Health (NIH), Persiaran Setia Murni U13/52, Setia Alam, 40170 Shah Alam, Selangor, Malaysia

*Corresponding author: Sarbhan Singh. This email address is being protected from spambots. You need JavaScript enabled to view it.

CITATION: Singh, S., Md. Iderus, N. H., Mohd Ghazali, S., Ahmad, L. C. R. Q., Mohd Ghazali, N., Md Nadzri, M. N., Zulkifli, A. A., Kamarudin, M. K., Huey, T. C., Hoon, C. C., & Cheng, L. M. (2026). Predictors of mental health literacy among adolescents using a random forest model: a machine learning approach. International Medical Research Journal, 12(1), 95–106. https://doi.org/10.63719/imrj.2026.12.01.008

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ABSTRACT

Mental health literacy (MHL) plays a critical role in early identification and help-seeking for mental disorders, especially among adolescents, a group facing a rising burden of depression and psychological distress globally. Traditional statistical approaches may overlook complex interactions among predictors. This study aimed to identify key predictors of MHL using a machine learning-based approach, specifically the Random Forest Model (RFM). A cross-sectional dataset comprising 1707 Malaysian adolescents with complete responses on 10 sociodemographic and psychosocial variables was analysed. MHL was dichotomised into Adequate and Inadequate categories. The dataset was split into training (80%) and testing (20%) subsets. Model development, hyperparameter tuning, and performance evaluation were conducted using R statistical software, with 5-fold cross-validation repeated three times. The final model was assessed on the test dataset using accuracy, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), F1 score, and Area Under the Curve (AUC). The final RFM, configured with 100 trees (mtry = 4, min.node.size = 1), achieved an AUC of 0.943 on the test dataset. The model achieved an accuracy of 87.1%, sensitivity of 90.0%, specificity of 87.0%, PPV of 17.0%, and F1 score of 0.29. Age emerged as the most important predictor, followed by Parental supervision and alcohol consumption. The RFM demonstrated high overall discrimination in classifying MHL levels. However, the relatively low precision suggests limited accuracy in identifying true positive cases, likely due to class imbalance, and findings should therefore be interpreted cautiously.

KEYWORDS: mental health literacy, machine learning, Random Forest model, predictors