用SVM模型预测员工倦怠风险，支持组织早期干预。
Support Vector Machine-Based Burnout Risk Prediction with an Interactive Interface for Organizational Use
- 基于SVM构建预测模型，利用30折交叉验证评估性能
- 模型R2达0.84，显著优于KNN与随机森林
- 开发交互式界面，便于非技术人员使用
倦怠是一种以情绪耗竭、去人格化和成就感降低为特征的心理综合征，对个人福祉和组织绩效有重大影响。本研究采用机器学习方法，基于HackerEarth员工倦怠挑战数据集预测倦怠风险。评估了三种监督学习算法：K近邻（KNN）、随机森林和支撑向量机（SVM），通过30折交叉验证并以决定系数（R²）衡量模型性能。在所测试模型中，SVM表现最佳（R² = 0.84），经配对t检验确认其显著优于KNN与随机森林。为确保实际应用性，使用Streamlit开发了交互式界面，使非技术用户可输入数据并获取倦怠风险预测。结果表明，机器学习可有效支持倦怠的早期识别，推动组织层面的数据驱动心理健康策略。
原文摘要 · Abstract (English)
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.
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