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Purpose - Inefficient hiring may result in lower productivity and highertraining costs. Productivity losses caused by absenteeism at work cost U.S.employers billions of dollars each year. Also, employers typically spend aconsiderable amount of time managing employees who perform poorly. The purposeof this study is to develop a decision support tool to predict absenteeismamong potential employees. Design/methodology/approach - We utilized a popularopen-access dataset. In order to categorize absenteeism classes, the data havebeen preprocessed, and four methods of machine learning classification havebeen applied: Multinomial Logistic Regression (MLR), Support Vector Machines(SVM), Artificial Neural Networks (ANN), and Random Forests (RF). We selectedthe best model, based on several validation scores, and compared itsperformance against the existing model; we then integrated the best model intoour proposed web-based for hiring managers. Findings - A web-based decisiontool allows hiring managers to make more informed decisions before hiring apotential employee, thus reducing time, financial loss and reducing theprobability of economic insolvency. Originality/value - In this paper, wepropose a model that is trained based on attributes that can be collectedduring the hiring process. Furthermore, hiring managers may lack experience inmachine learning or do not have the time to spend developing machine learningalgorithms. Thus, we propose a web-based interactive tool that can be usedwithout prior knowledge of machine learning algorithms.

Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees