Conference Paper2025

Analyzing Machine Learning Models for Multiclass Classification of Human Stress Levels: Comparison Between Balanced and Imbalanced Data

Nabarun Halder, Armun Alam, Jahanggir Hossain Setu, Asif Mahmood, Ashraful Islam, M Ashraful Amin

2025 17th International Conference on Knowledge and Smart Technology (KST)

IEEE, pp. 104-109

  • Good Health and Well-being — Relevance: 81%
DOI: 10.1109/kst65016.2025.11003364URL

Abstract

Stress is a psychological strain with diverse causes that can significantly impact health, potentially leading to conditions such as depression, heart disease, and impaired coping abilities if prolonged. While excessive stress is often harmful, moderate levels, known as eustress, can enhance motivation and performance. This study uses a publicly available dataset containing responses from 843 participants, aged 14 to 100, who rated their stress levels as distress, eustress, or no stress based on lifestyle and personal factors such as academic workload, social connections, and self-confidence. To classify these stress types, six machine learning models were developed: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), and a Multilayer Perceptron (MLP). During pre-processing, we identified a significant class imbalance, which could skew the results. To address this, the Synthetic Minority Oversampling Technique (SMOTE) was applied, ensuring a more balanced dataset and improving model reliability for underrepresented classes. Among the models, the MLP, specifically the Ablation${ }_{2}$architecture, achieved the highest performance, with an accuracy of 97.44 % and an F1-score of 97.36 % on the resampled dataset. While most models showed improved results after resampling, KNN's performance declined from 94.44 % to$\mathbf{7 7. 7 8 \%}$. This study highlights the importance of data balancing and model architecture in accurately classifying stress levels.

Keywords

  • distress
  • k-nearest neighbors
  • class imbalance
  • Logistic regression
  • multi-class classification
  • Support vector machine
  • Multilayer perceptron
  • SMOTE
  • eustress
  • Decision tree

CCDS Authors

Nabarun Halder
Nabarun Halder

Nabarun Halder

  • Research Assistant, CCDS

Published at

UbiComp, IJCNN, HCII, Pattern Recognition, IEEE Access

Armun Alam
Armun Alam

Armun Alam

  • Alumni, CCDS

  • Master's Student

    McGill University, Canada

  • BSc

    Independent University, Bangladesh (IUB)

Published at

ECCV, Scientific Reports

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Published at

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

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

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