A Model-Mediated Stacked Ensemble Approach for Depression Prediction Among Professionals
Journal:
arXiv
Published Date:
Jun 17, 2025
Abstract
Depression is a significant mental health concern, particularly in
professional environments where work-related stress, financial pressure, and
lifestyle imbalances contribute to deteriorating well-being. Despite increasing
awareness, researchers and practitioners face critical challenges in developing
accurate and generalizable predictive models for mental health disorders.
Traditional classification approaches often struggle with the complexity of
depression, as it is influenced by multifaceted, interdependent factors,
including occupational stress, sleep patterns, and job satisfaction. This study
addresses these challenges by proposing a stacking-based ensemble learning
approach to improve the predictive accuracy of depression classification among
professionals. The Depression Professional Dataset has been collected from
Kaggle. The dataset comprises demographic, occupational, and lifestyle
attributes that influence mental well-being. Our stacking model integrates
multiple base learners with a logistic regression-mediated model, effectively
capturing diverse learning patterns. The experimental results demonstrate that
the proposed model achieves high predictive performance, with an accuracy of
99.64% on training data and 98.75% on testing data, with precision, recall, and
F1-score all exceeding 98%. These findings highlight the effectiveness of
ensemble learning in mental health analytics and underscore its potential for
early detection and intervention strategies.
