Support Vector Machine-Based Burnout Risk Prediction with an Interactive Interface for Organizational Use

TL;DR

This paper develops an SVM-based machine learning model to predict employee burnout risk, demonstrating high accuracy and providing an interactive tool for organizational use to support mental health strategies.

Contribution

It introduces a novel SVM-based burnout prediction model with an interactive interface for practical organizational application.

Findings

SVM achieved the highest R2 of 0.84 among tested models.

The interactive tool enables non-technical users to predict burnout risk.

Machine learning shows promise for early burnout detection in organizations.

Abstract

Burnout is a psychological syndrome marked by emotional exhaustion, depersonalization, and reduced personal accomplishment, with a significant impact on individual well-being and organizational performance. This study proposes a machine learning approach to predict burnout risk using the HackerEarth Employee Burnout Challenge dataset. Three supervised algorithms were evaluated: nearest neighbors (KNN), random forest, and support vector machine (SVM), with model performance evaluated through 30-fold cross-validation using the determination coefficient (R2). Among the models tested, SVM achieved the highest predictive performance (R2 = 0.84) and was statistically superior to KNN and Random Forest based on paired $t$-tests. To ensure practical applicability, an interactive interface was developed using Streamlit, allowing non-technical users to input data and receive burnout risk predictions. The results highlight the potential of machine learning to support early detection of burnout and promote data-driven mental health strategies in organizational settings.