Predicting Absenteeism at Work Using Machine Learning Algorithms

Print Friendly, PDF & Email

Muthanna Journal of Pure Sciences – MJPS

VOL.(7), NO.(1), 2020

Al Muthanna University, Almuthanna, Alsamawa, Iraq

*Corresponding Author :

DOI: 10.52113/2/07.01.2020/1-12


To work in the commercial environment, the company needs to be a major competitor in the
business market, which depends mainly on the company’s resources. One of the most important
resources is the employees. Based on that, the absence of the employees from work leads to
deterioration and reduce production in the institutions which leads to heavy losses. There are many
reasons why employees are absent from work. Those may include health problems and social
occasions. The purpose of this paper was to apply machine learning techniques to predict the
absenteeism at work. There are four methods have been used in this research ( neural network(NN)
technique ,decision tree (DT) technique, support vector machine (SVM) technique and logistic
regression (LR) technique. . decision tree model has the highest accuracy equals to 83.33% with AUC
0.834 and the support vector machine has the lowest accuracy equals to 68.47 % with AUC 0.760.


Decision Tree; SVM; ANN; Logistic Regression; Predicting Absenteeism at Work

Down load full article/PDF

One thought on “Predicting Absenteeism at Work Using Machine Learning Algorithms”

  1. One other thing is that an online business administration training course is designed for college students to be able to without problems proceed to bachelors degree courses. The 90 credit education meets the other bachelor college degree requirements so when you earn your associate of arts in BA online, you may have access to the newest technologies in this field. Some reasons why students are able to get their associate degree in business is because they may be interested in the field and want to get the general instruction necessary just before jumping right into a bachelor diploma program. Thx for the tips you provide as part of your blog.

Leave a Reply

Your email address will not be published.