Name
Title | ||
|---|---|---|
Incorporating a Machine Learning Model into a Web-Based Administrative Decision Support Tool for Predicting Workplace Absenteeism |
Abstract | ||
|---|---|---|
Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. In addition, employers typically spend a considerable amount of time managing employees who perform poorly. By using predictive analytics and machine learning algorithms, organizations can make better decisions, thereby increasing organizational productivity, reducing costs, and improving efficiency. Thus, in this paper we propose hybrid optimization methods in order to find the most parsimonious model for absenteeism classification. We utilized data from a Brazilian courier company. In order to categorize absenteeism classes, we preprocessed the data, selected the attributes via multiple methods, balanced the dataset using the synthetic minority over-sampling method, and then employed four methods of machine learning classification: Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), Artificial Neural Network (ANN), and Random Forest (RF). We selected the best model based on several validation scores, and compared its performance against the existing model. Furthermore, project managers may lack experience in machine learning, or may not have the time to spend developing machine learning algorithms. Thus, we propose a web-based interactive tool supported by cognitive analytics management (CAM) theory. The web-based decision tool enables managers to make more informed decisions, and can be used without any prior knowledge of machine learning. Understanding absenteeism patterns can assist managers in revising policies or creating new arrangements to reduce absences in the workplace, financial losses, and the probability of economic insolvency. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/info13070320 | INFORMATION |
Keywords | DocType | Volume |
absenteeism, multi-class classifications, multinomial logistic regression, support vector machines, random forests, artificial neural networks | Journal | 13 |
Issue | ISSN | Citations |
7 | 2078-2489 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gopal Nath | 1 | 0 | 0.34 |
| Yawei Wang | 2 | 0 | 0.34 |
| Austin Coursey | 3 | 0 | 0.34 |
| Krishna K. Saha | 4 | 0 | 0.34 |
| Srikanth Prabhu | 5 | 0 | 0.34 |
| Saptarshi Sengupta | 6 | 0 | 1.01 |