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

• URL: http://arxiv.org/abs/2506.14459v1
• Date: Tue, 17 Jun 2025 12:19:40 GMT
• Title: A Model-Mediated Stacked Ensemble Approach for Depression Prediction Among Professionals
• Authors: Md. Mortuza Ahmmed, Abdullah Al Noman, Mahin Montasir Afif, K. M. Tahsin Kabir, Md. Mostafizur Rahman, Mufti Mahmud,
• Abstract summary: Depression is a significant mental health concern, particularly in professional environments.<n>This study proposes a stacking-based ensemble learning approach to improve the predictive accuracy of depression classification among professionals.
• Score: 2.2756398880750055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs.<n>Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder.<n>Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
  Mental health disorders are one of the most serious diseases in the world.<n>Privacy concerns limit the accessibility of personalized treatment data.<n>MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study [0.6524460254566905]
  Depression has affected millions of people worldwide and has become one of the most common mental disorders. Recent research has evidenced that machine learning (ML) and Natural Language Processing (NLP) tools and techniques have significantly been used to diagnose depression. However, there are still several challenges in the assessment of depression detection approaches in which other conditions such as post-traumatic stress disorder (PTSD) are present.
  arXiv  Detail & Related papers  (2024-04-03T19:45:40Z)
• Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey [62.31381724639944]
  Stress and depression are interrelated and together they impact engagement in daily tasks.<n>This survey is the first to simultaneously explore computational methods for analyzing stress, depression and engagement.
  arXiv  Detail & Related papers  (2024-03-09T11:16:09Z)
• PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
  Psychological measurement is essential for mental health, self-understanding, and personal development. PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
  arXiv  Detail & Related papers  (2024-02-19T18:00:30Z)
• Exploration of Adolescent Depression Risk Prediction Based on Census Surveys and General Life Issues [7.774933303698165]
  The prevalence of depression among adolescents is steadily increasing. Traditional diagnostic methods, which rely on scales or interviews, prove particularly inadequate for detecting depression in young people. We introduce a method for managing severely imbalanced high-dimensional data and an adaptive predictive approach tailored to data structure characteristics.
  arXiv  Detail & Related papers  (2024-01-06T09:14:25Z)
• Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting [64.80538055623842]
  sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give. We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
  arXiv  Detail & Related papers  (2023-09-13T15:42:06Z)
• Precision psychiatry: predicting predictability [0.0]
  I review ten challenges in the field of precision psychiatry. Need for studies on real-world populations and realistic clinical outcome definitions. Consider treatment-related factors such as placebo effects and non-adherence to prescriptions.
  arXiv  Detail & Related papers  (2023-06-21T13:10:46Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• Bias Reducing Multitask Learning on Mental Health Prediction [18.32551434711739]
  There has been an increase in research in developing machine learning models for mental health detection or prediction. In this work, we aim to perform a fairness analysis and implement a multi-task learning based bias mitigation method on anxiety prediction models. Our analysis showed that our anxiety prediction base model introduced some bias with regards to age, income, ethnicity, and whether a participant is born in the U.S. or not.
  arXiv  Detail & Related papers  (2022-08-07T02:28:32Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.