# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
###### Abstract  
In today's dynamic business world, organizations are increasingly relying on innovative technologies to improve the efficiency and effectiveness of their human resource (HR) management. Our study uses historical time and attendance data collected with the MojeUre time and attendance system to predict employee absenteeism, including sick and vacation leave, using machine learning methods. We integrate employee demographic data and the absence profiles on timesheets showing daily attendance patterns as fundamental elements for our analysis. We also convert the absence data into a feature-based format suitable for the machine learning methods used. Our primary goal in this paper is to evaluate how well we can predict sick leave and vacation leave over short- and long-term intervals using tree-based machine learning methods based on the predictive clustering paradigm. This paper compares the effectiveness of these methods in different learning settings and discusses their impact on improving HR decision-making processes. machine learning in human resource management; predictive modelling; tree-based methods; feature engineering; multi-target prediction; employee absence prediction; data-driven human resources decision making +
Footnote †: journal: Medical  
is critical to aligning staff availability with the needs of the business to optimize staff and resource allocation [3]. The use of predictive analytics to forecast employee absences offers significant strategic advantages. By enabling proactive organization and staffing, these tools help companies anticipate and resolve staff shortages before they disrupt operations. Predictive insights enable strategic staff redeployment, helping to balance workloads and maintain productivity across all departments. This proactive approach not only minimizes the impact of business disruption, but also improves organizational adaptability, allowing companies to respond quickly to changes in the work environment. The further integration of predictive modeling into human resource information systems (HRIS) enables organizations to use these tools as part of a comprehensive decision support system (DSS) that provides HR leaders with actionable insights for proactive people management [4]. Seamless integration with existing HR information systems ensures real-time data flow and updates and enables predictions based on the latest data. For example, if the system detects an increased risk of absenteeism, it can automatically notify managers and suggest preventative actions [5]. In addition, a user-friendly dashboard that visualizes key metrics and predictions can significantly improve the user experience for HR managers by stimulating preventive discussions to resolve potential issues or coordinate appropriate staffing. Such tools support better planning, greater efficiency, and a more agile response to dynamic changes in the workforce, in line with best practices in workforce analytics and workforce planning [4,5]. **Motivation. ** The increasing complexity of HR management has necessitated the development of advanced analytical tools to effectively predict employee absenteeism. Our previous study [6], served as the basis for exploring the demand for automated absence forecasting. This study has shown that organizations have a strong interest in using historical data to accurately predict employee absence, particularly to predict short-term absences such as sick leave and vacation leave up to a week in advance. The research utilized a survey approach targeting users of the MojeUre system to identify the key features desired in an absence prediction system. The results showed that organizations place a high value on the ability to accurately predict absences within a one-week time frame with minimal errors, ideally within two days. Such features would allow companies to proactively arrange for replacement staff and optimize work allocation to mitigate the impact of unplanned absences.