Conference Paper2024Cited by 3

Ensemble and Deep Learning Approaches for Automated Screening of Anxiety, Depression, and Burnout in Medical Student Populations

Sami Rashid, Tasnuba Badrul, Lishan Rafid, Jahanggir Hossain Setu, Nabarun Halder, Ashraful Islam

IEEE 2024 International Advances in Science & Engineering Technology (ASET) Multi-Conferences

IEEE, pp. 01-10

  • Good Health and Well-being — Relevance: 95%
DOI: 10.1109/aset60340.2024.10708680URL

Abstract

Mental health issues are increasingly affecting peo-ple worldwide. Efforts are taken to address these issues, however, similar importance is not always taken for all demographics. Medical students are particularly susceptible to being impacted by anxiety, depression, and burnout due to the intense pressures of their educational and practical settings. These mental health problems become worse because of the demanding workload of medical school, the emotional toll of patient care, and the competitive atmosphere. These problems have adverse effects on student's academic performance, personal wellness, and future prospective role as empathetic healthcare providers, which re-inforces the need for introduction of special care and interventions in medical education. This study uses a dataset of 886 Swiss medical students to automate the screening procedure for anxiety, depression, and burnout using Machine Learning (ML) and Deep Learning (DL) approaches. The analysis juxtaposes the performance of two advanced computational models: an Ensemble classifier, integrating Random Forest (RF), Naive Bayes (NB), and Light Gradient-Boosting Machine (LightGBM), and a Deep Neural Network (DNN). The study's cornerstone lies in its evaluation of these models' predictive prowess, underpinned by meticulous feature selection via Information Gain and an ablation study. The DNN model emerges as a frontrunner demonstrating accuracy rates of 81.4 % for depression, 76.65 % for anxiety, and 73.59 % for burnout. Comparative analyses further validate the DNN's efficacy against the Ensemble classifier, thereby providing a promising method for automated clinical diagnosis for mental health professionals. Thus, the ultimate objective is to bridge the gap between undetected mental health issues and accessible, effective care highlighting the indispensable role of Artificial Intelligence (AI) in shaping the future of mental health services.

Keywords

  • depression screening
  • medical students
  • naive Bayes
  • information gain feature selection
  • ensemble classifiers
  • Burnout
  • deep neural network
  • student mental health
  • Random forest
  • automated mental health screening

CCDS Authors

Sami Rashid
Sami Rashid

Sami Rashid

  • Intern, CCDS

Research Interests

Machine Learning

Nabarun Halder
Nabarun Halder

Nabarun Halder

  • Research Assistant, CCDS

Published at

UbiComp, IJCNN, HCII, IEEE Access

Research Interests

Machine Learning, Deep Learning, Natural Language Processing, Health Informatics, Human-Computer Interaction

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

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Cited By

  1. 1.Eldar Yeskuatov, Lee Kien Foo, Sook-Ling Chua. (2025). Detecting Burnout Among Undergraduate Computing Students with Supervised Machine Learning. Healthcare, 13(23), 3182[10.3390/healthcare13233182]
  2. 2.Reza Salehinia, Sajjad Salehian, Marzieh Nasiri Sangari, Mohammad Amin Nasiri Sangari, Hossein Abbassian. (2026). Exploratory analysis of machine learning models for state and trait anxiety based on Spielberger questionnaire data in nursing students. BMC Medical Education, 26(1)[10.1186/s12909-026-08842-3]
  3. 3.Nureen Afiqah Mohd Zaini, Nur Syafiqah M. Nafis, Suryanti Awang, Putu Harry Gunawan, Mohammed Ahmed Talab. (2025). Burnout Prediction: A Comparative Analysis of Machine Learning and Deep Learning Approaches. , 73–78[10.1109/icsecs65227.2025.11279133]
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