A Model-Mediated Stacked Ensemble Approach for Depression Prediction Among Professionals

TL;DR

This paper introduces a stacking ensemble model that significantly improves depression prediction accuracy among professionals by integrating diverse learners and a logistic regression mediator, demonstrating high performance on a Kaggle dataset.

Contribution

It presents a novel stacking-based ensemble approach tailored for depression prediction, effectively capturing complex factors influencing mental health in professional settings.

Findings

Achieved 99.64% accuracy on training data

Achieved 98.75% accuracy on testing data

Precision, recall, and F1-score all exceed 98%

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.