Balancing the Scales: Employment Status, Educational Background, and Machine Learning in Predicting Student Anxiety

DOI

10.2991/978-94-6463-704-5_4How to use a DOI?

Keywords

Anxiety; Statistics; Machine Learning; Random Forest; Logistic Regression

Abstract

The issue of student anxiety remains an essential area of concern in academia and several reasons, like employment and educational levels, fuel it. This study gives a fresh perspective on how these considerations contribute to increasing or decreasing the levels of anxiety and implementing machine learning models for making predictive decisions involving anxiety. Frequentist hypothesis-testing statistical procedures with ANOVA and correlation tests showed the means differed by employment status shoulder to shoulder to (F = 51.302, p = 5.83e-33) and level of education (F = 15.633 p = 3.82e-10) level. The existing literature outline in this research presented the diversity of machines learning models in use extending to Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Decision Tree for increasing both prediction accuracy and efficiency. Out of these statistical analysis tools used, logistic regression was found performing better at 82% prediction who forecasted psychotic symptoms accuracy among the sample while random forest was quite equal on all factors in where outcoming performance of all metrics were tested. Such findings support the hypothesis of machine learning integration into school intervention practices for students’ mental health, suggesting new opportunities for prevention and care.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Ashvini Alashetty
AU  - Saliha Bathool
AU  - Jagdish Chandra Patni
PY  - 2025
DA  - 2025/04/30
TI  - Balancing the Scales: Employment Status, Educational Background, and Machine Learning in Predicting Student Anxiety
BT  - Proceedings of the International Conference on Smart Health and Intelligent Technologies (ICSHit-2024)
PB  - Atlantis Press
SP  - 18
EP  - 32
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-704-5_4
DO  - 10.2991/978-94-6463-704-5_4
ID  - Alashetty2025
ER  -