# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 2 Background and Related Work
### Related Work  
In the growing field of human resources analytics, the use of machine learning to predict absenteeism of employees is becoming increasingly important for effective workforce management. This section looks at a number of studies that enhance our understanding of how predictive analytics can be used in HR. We explore a wide range of topics, from the use of neural networks to the impact of demographic factors and the identification of risk factors for absenteeism. The literature is divided into topics such as the use of advanced computational models, analysis of long-term data, demographic influences, risk factors, ethical considerations, impact on employee engagement and practical applications of HR analytics. Together, these topics deepen our understanding of how historical data can be used to predict employee absenteeism. This closely aligns with the focus of our work on predicting future absenteeism using machine-learning techniques based on historical patterns. In **exploring advanced modeling techniques** within HR, [PERSON] et al. [20] conducted a comprehensive evaluation of three neural network models - Backpropagation, Radial Basis Function, and Long Short-Term Memory (LSTM) - to predict absenteeism of employees. Their study highlights the superior performance of the LSTM model, which achieves near-perfect prediction accuracy in complex data scenarios. This emphasises the potential of advanced neural network architectures in processing detailed absence patterns and demonstrates their adaptability to complicated HR data. Similarly, [PERSON] et al. [21] address the challenge of predicting employee absence risk using various machine learning methods (SVM), including neural networks, random forest, and support vector machines. They investigated the impact of training data size and different feature sets on model performance and introduced a method to rank the sensitivity of neural networks to individual features, providing valuable insight to improve predictive models. In general, the integration of deep learning methods, as proposed in recent [22; 23] could improve prediction performance, especially when capturing complex patterns and temporal dependencies within the data. [PERSON] et al. [24] discuss a novel deep neural network (DNN) model to predict employee absenteeism using data from a Brazilian courier company. The DNN achieved a 97.5% accuracy, outperforming traditional models such as Decision Trees and SVM, highlighting its potential to help organizations manage absenteeism effectively. [PERSON] et al. [25] investigate deep learning models to predict absenteeism among public security agents, finding that Multilayer Perceptrons (MLP) achieved the highest accuracy at 78%, demonstrating the applicability of these models in public security contexts. [PERSON] [26] uses multiple regression to examine **demographic influences on employee absenteeism**. More specifically, the author examines the effects of age and seniority on employee absenteeism in different work groups within the UK local government. The study identifies linear and curvilinear relationships, with seniority moderating the impact of age on absence, contributing to a more nuanced understanding of the impact of demographic factors on workplace attendance. [PERSON] et al. [27] focus on **identifying the most important predictors of long-term and frequent sick leave absence** among airline employees. Their analysis, using logistic regression, identifies factors such as age, pregnancy, working conditions, and previous absenteeism as significant predictors and provides practical insights into the risk factors that can predict sickness absence with considerable accuracy. In addition, [PERSON] [28] presents a systematic analysis of machine learning models used to predict absenteeism and temporary disability. Reviewing research from 2010 onwards, the study highlights artificial neural networks (ANNs) as the most effective and widely used models, particularly in Brazil and India, but also in countries such as Saudi Arabia and Australia.