  Purpose - Inefficient hiring may result in lower productivity and higher
training costs. Productivity losses caused by absenteeism at work cost U.S.
employers billions of dollars each year. Also, employers typically spend a
considerable amount of time managing employees who perform poorly. The purpose
of this study is to develop a decision support tool to predict absenteeism
among potential employees. Design/methodology/approach - We utilized a popular
open-access dataset. In order to categorize absenteeism classes, the data have
been preprocessed, and four methods of machine learning classification have
been applied: Multinomial Logistic Regression (MLR), Support Vector Machines
(SVM), Artificial Neural Networks (ANN), and Random Forests (RF). We selected
the best model, based on several validation scores, and compared its
performance against the existing model; we then integrated the best model into
our proposed web-based for hiring managers. Findings - A web-based decision
tool allows hiring managers to make more informed decisions before hiring a
potential employee, thus reducing time, financial loss and reducing the
probability of economic insolvency. Originality/value - In this paper, we
propose a model that is trained based on attributes that can be collected
during the hiring process. Furthermore, hiring managers may lack experience in
machine learning or do not have the time to spend developing machine learning
algorithms. Thus, we propose a web-based interactive tool that can be used
without prior knowledge of machine learning algorithms.
