Optimizing IT Remote Workers Mental Health Prediction using Feature Engineering

Fikri Muhamad Fahmi ; Budiman Budiman ; Nur Alamsyah
International Journal of Science and Mathematics E... 📅 2025 Vol 2, No 2 🔗 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.
Keywords
Random Forest; Classification; Machine Learning; Mental Health
How to Cite

Fikri Muhamad Fahmi, et al. (2025). Optimizing IT Remote Workers Mental Health Prediction using Feature Engineering. International Journal of Science and Mathematics Education, 2(2). https://doi.org/10.62951/ijsme.v2i2.193

Fikri Muhamad Fahmi; Budiman Budiman; Nur Alamsyah, "Optimizing IT Remote Workers Mental Health Prediction using Feature Engineering," International Journal of Science and Mathematics Education, vol. 2, no. 2, 2025.

Fikri Muhamad Fahmi; Budiman Budiman; Nur Alamsyah. "Optimizing IT Remote Workers Mental Health Prediction using Feature Engineering." International Journal of Science and Mathematics Education, vol. 2, no. 2, 2025.

Fikri Muhamad Fahmi; Budiman Budiman; Nur Alamsyah. "Optimizing IT Remote Workers Mental Health Prediction using Feature Engineering." International Journal of Science and Mathematics Education 2, no. 2 (2025).

Fikri Muhamad Fahmi, et al. (2025) 'Optimizing IT Remote Workers Mental Health Prediction using Feature Engineering', International Journal of Science and Mathematics Education, 2(2). doi: 10.62951/ijsme.v2i2.193.

Fikri Muhamad Fahmi; Budiman Budiman; Nur Alamsyah. Optimizing IT Remote Workers Mental Health Prediction using Feature Engineering. International Journal of Science and Mathematics Education. 2025;2(2).

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