Abstract

This dissertation explores the application of machine learning in mental health risk prediction of remote workers, which is increasingly becoming a significant issue, with the practise of flexible working reshaping organisational practises. It is based on the theoretical framework that has been used previously such as Job Demands Resources model and Stress-Strain model which emphasise the role of workload, support systems and personal resources in determining the well-being of employees. The context of the study indicates the growing rate of remote and hybrid employment, and the associated increase in the number of issues associated with stress and isolation, as well as mental pressure. The core research questions were considered to be the factors that affect mental health risk in remote workers, the effectiveness of different machine learning models to forecast the risk of these factors, and the predictive insights which can benefit organisational well-being strategies. A virtual survey dataset which included 5 000 samples of records of remote, hybrid, and onsite workers was employed to guarantee that the ethical consideration is still feasible but also record realistic patterns within the workplace. That analysis included cleaning, and preprocessing, encoding, and feature engineering data to get the dataset ready to modelling. Various algorithms of machine learning were applied, which are Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, and Support Vector Machines. Accuracy, precision, recall, F1-score, and ROC-AUC were used as the evaluation models to identify the model that is best performing in terms of classifying mental health risk. The results showed that all of the models were very effective and consistent in recognising individuals that were identified as being at risk of mental health with a number of models having high recall and F1-scores. Such variables as the level of stress, the quality of sleep, social isolation, and satisfaction with remote work turned out to be significant predictors of risk. These findings support the theoretical knowledge that the psychological well being of the remote setting is influenced by both personal and organisational variables. The research has concluded that machine learning can offer a working solution to the early detection of employees who might need some support in order to apply more proactive to well-being measures in organisations. This and other patterns present in this synthetic environment should be tested in future studies through the use of real-world data or longitudinal data, which ii the dissertation recommends using. It also proposes the use of qualitative data to improve the contextual knowledge. To the organisational practise, the findings promote the incorporation of both data-driven well-being monitoring solutions and more conventional HR approaches to enhance the utilisation of remote and hybrid work-based employees.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ioannis Karamitsos

Campus

RIT Dubai

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