This paper describes a decision support system designed for a Belgian Human Resource (HR) and Well-Being Service Provider. Their goal is to improve health and well-being in the workplace, and to this end, the task is to identify groups of employees at risk of sickness absence who can then be targeted with interventions aiming to reduce or prevent absences. To facilitate deployment, we apply a range of existing machine-learning methods to obtain predictions at monthly intervals using real HR and payroll data that contains no health-related predictors. We model employee absence as a binary classification problem with loss asymmetry and conceptualise a misclassification cost matrix of employee sickness absence. Model performance is evaluated using cost-based metrics, which have intuitive interpretation. We also demonstrate how this problem can be approached when costs are unknown. The proposed flexible evaluation procedure is not restricted to a specific model or domain and can be applied to address other HR analytics questions when deployed. Our approach of considering a wider range of methods and cost-based performance evaluation is novel in the domain of absenteeism prediction.

本文描述了为比利时人力资源 (HR) 和福利服务提供商设计的决策支持系统。他们的目标是改善工作场所的健康和福祉，为此，任务是确定有病假风险的员工群体，然后可以针对他们采取旨在减少或防止缺勤的干预措施。为了促进部署，我们应用了一系列现有的机器学习方法，使用不包含健康相关预测因素的真实 HR 和工资数据，以每月为间隔获得预测。我们将员工缺勤建模为具有损失不对称性的二元分类问题，并将员工生病缺勤的错误分类成本矩阵概念化。使用基于成本的指标评估模型性能，这些指标具有直观的解释。我们还演示了在成本未知时如何解决这个问题。提议的灵活评估程序不限于特定模型或领域，并且可以在部署时应用于解决其他 HR 分析问题。我们考虑更广泛的方法和基于成本的绩效评估的方法在缺勤预测领域是新颖的。