# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 7 Conclusions and Future Work  
Our study emphasises the effectiveness of ensemble methods, especially the bagging method, in predicting employee absenteeism across different prediction intervals. For one-week-ahead predictions, the bagging method proves to be the most accurate for vacation leave, while Random Forest performs best to predict sick leave. For predictions two weeks in advance, optimal results are achieved when the predictions are made one week in advance using these specific methods. Even for one-month-ahead predictions, bagging continues to show superior performance for vacation leave, and Random Forest remains effective for sick leave. The comparative analysis of single-target and multi-target predictions emphasizes the robustness of bagging for different prediction tasks and time periods. Vacation leave predictions tend to be more reliable and straightforward, attributed to the regularity and predictability of the historical data, which often includes plannedabsences. The clarity of these patterns enhances the performance of methods like Bagging, making it particularly suited for modeling vacation leave. As part of our future research, we will investigate different scenarios for predictive modeling with the prepared datasets. We plan to investigate the selective predictive analysis of absenteeism by identifying critical weeks influenced by factors such as seasonality, proximity to public holidays, and job-specific requirements. In addition, regional and domain-specific analyses will examine how absence predictions differ by location and type of organization. More detailed time-based comparisons--from daily to monthly predictions--will also be analysed using hierarchical regression and multi-label classification techniques to predict detailed weekly employee profiles. To adapt to the changes in working patterns after the COVID-19 pandemic, including the increase in remote working, we want to integrate post-pandemic data into our models. In addition, a real-time analytics tool will be developed that can be integrated into HR systems to enable proactive identification and management of irregular absence patterns. Improving the interpretability of the model and creating user-friendly dashboards are also crucial to promote trust and improve the practical use of such a tool in the organisational environment to support decision-making. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14167037/s1](https://www.mdpi.com/article/10.3390/app14167037/s1)]([https://www.mdpi.com/article/10.3390/app14167037/s1](https://www.mdpi.com/article/10.3390/app14167037/s1)). All detailed experimental results (presented in table format) that are referenced in the text of the manuscript are available in the Supplementary Material and it contains the following tables: Table S1: Results for one-week-ahead vacation leave prediction; Table S2: Results for two-week-ahead vacation leave prediction; Table S3: Results for one month ahead vacation leave prediction; Table S4: RMSE results comparing single-target and multi-target models for vacation leave prediction; Table S5: Result CC measure for single-target vs. multi-target vacation leave prediction; Table S6: Results for one-week ahead sick leave prediction; Table S7: Results for two-week ahead sick leave prediction; Table S9: Result RMSE measure for single-target vs. multi-target sick leave prediction; Table S10: Result CC measure for single-target vs. multi-target sick leave prediction. Conceptualization, P.Z. and P.P. ; methodology, P.P. ; software, P.Z. ; validation, P.Z. and P.P. ; investigation, P.P. ; resources, P.Z. ; data curation, P.Z. ; writing--original draft preparation, P.Z. ; writing--review and editing, P.P. ; visualization, P.Z. and P.P. ; supervision, P.P. All authors have read and agreed to the published version of the manuscript. [PERSON] is supported by the company 1A Internet, d.o.o. and [PERSON] is supported by the Slovenian Research and Innovation Agency through program group P2-0103. Not applicable.