Solidity of companies or institutions is related to several factors but mostly to absenteeism. Taking annual leave or pre-determined absent days of personnel may be covered by others however, unexpected absenteeism causes irredeemably poor results. Prediction of the correlation between this predetermined and unexpected absenteeism is a challenging task and includes non-linear relationship. Neural Network based Machine Learning models are built to solve this kind of non-linear problems by using their non-deterministic nature. In this research, three neural network models; Backpropagation, Radial Basis Function and Long-Short Term Memory neural networks, are implemented to solve prediction problem of absenteeism. In addition, a comparative study is conducted between these models. Two experiments with different training ratios and three evaluation criteria are considered and implemented. The experimental results suggested that Long-Short Term Memory neural network has very high prediction rates as 99.9% in prediction problems that consists complex data and it produced superior results than other two neural network models.
CITATION STYLE
Dogruyol, K., & Sekeroglu, B. (2020). Absenteeism prediction: A comparative study using machine learning models. In Advances in Intelligent Systems and Computing (Vol. 1095 AISC, pp. 728–734). Springer. https://doi.org/10.1007/978-3-030-35249-3_94
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