A Bayesian neural network approach for predicting depression risk in adolescents

Adolescent depression is a significant public health concern in low- and middle-income countries, including Kenya, where limited screening capacity contributes to under diagnosis. This study developed and validated a Bayesian Neural Network for predicting depression risk among adolescents, leveraging probabilistic inference to capture predictive uncertainty and complex nonlinear relationships. Secondary data for 2,192 adolescents aged 12-18 years were obtained from the Open Science Framework, incorporating psychosocial, demographic, and mental health measures. Significant predictors were identified using chi-square tests and point-biserial correlations, followed by forward feature selection. The proposed Bayesian Neural Network employed a three-hidden-layer feedforward architecture with ReLU and sigmoid activations, Gaussian priors on weights, and Bayes-by-Backprop Variational inference. Model performance was benchmarked against a Random Forest classifier using cross-validation. Results indicate that anxiety, loneliness, perceived social support, gratitude, positive youth development, academic self-perception, gender, academic form, financial status, parental education, and age are significant predictors of depression. The model achieved superior performance, with an accuracy of 80.13%, F1 score of 0.876, recall of 0.965, ROC-AUC of 0.813, and PR-AUC of 0.913, outperforming the Random Forest in most metrics except precision. Calibration analysis yielded a low Brier score (0.0693), indicating well-calibrated probabilistic predictions. These findings demonstrate the suitability of Bayesian Neural Networks for adolescent depression risk screening in resource-constrained settings, where uncertainty-aware decision support is critical.