Abstract

This study aims to predict and analyze burnout among healthcare workers using supervised machine learning techniques and Exploratory Data Analysis (EDA). Leveraging the Healthcare Workforce Mental Health Dataset, the research identifies key demographic, occupational, and psychological factors most strongly associated with burnout. The methodology involves data preprocessing, feature selection, and model training using algorithms such as logistic regression, decision trees, random forests, and gradient boosting. Model performance will be evaluated through standard metrics, including accuracy, precision, recall, and ROC-AUC. The expected outcome is a predictive framework that highlights high-impact burnout predictors and generates actionable insights to support early intervention and prevention strategies, thereby enhancing the overall well-being and resilience of the healthcare workforce.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research (Dubai)

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Comments

This thesis has been embargoed. The full-text will be available on or around 9/14/2026.

Campus

RIT Dubai

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