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All Journal International Journal of Science and Mathematics Education
Fikri Muhamad Fahmi
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Education Mathematics Social Sciences

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Optimizing IT Remote Workers Mental Health Prediction using Feature Engineering Fikri Muhamad Fahmi; Budiman Budiman; Nur Alamsyah
International Journal of Science and Mathematics Education Vol. 2 No. 2 (2025): June: International Journal of Science and Mathematics Education
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijsme.v2i2.193

Abstract

Given the increasing prevalence of mental health challenges in digital work settings, especially among IT remote workers, early detection mechanisms have become critically important. This study aims to improve the prediction accuracy of mental health conditions among IT remote workers by integrating feature engineering techniques within machine learning models. Five algorithms consisting of Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes were evaluated. The Random Forest model achieved the best performance, with 83% accuracy, 83% precision, 100% recall, and a 90% F1-score, followed closely by Logistic Regression with 82% accuracy. Nevertheless, the results demonstrate the feasibility of applying machine learning to support the early detection of mental health risks, offering a strong foundation for future research in predictive analytics and the development of intelligent support systems within digital work environments.
Co-Authors Budiman Budiman NUR ALAMSYAH