An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

Abstract

Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.

An Objective-Based Entropy Approach for Interpretable Decision Tree Models in Support of Human Resource Management: The Case of Absenteeism at Work

“…We use a workplace absenteeism dataset for the period between July 2007 and July 2010 from a courier company in Brazil. This 740-sample dataset, which is available at the UCI Machine Learning Repository [ 11 ], has been subject to previous investigations using various machine learning models (see, e.g., in [ 12 , 13 , 14 , 15 ]). Table 1 lists the 21 features of the dataset that reflect work-related and personal factors.…”

“…We now briefly review the main findings of machine learning models that have analyzed absenteeism, and in so doing, we highlight the contributions of the present study. To enable traceability and facilitate comparison with previous research, we use a dataset that was first introduced by [ 11 ] and has been subject to fairly extensive research (see, e.g., in [ 12 , 13 , 14 , 15 ]). Wahid et al [ 12 ], for example, employed various models, such as Decision Tree, Tree Ensemble, Gradient Boosted Tree, and Random Forest, to predict the absence time.…”

“…In terms of accuracy, their classification models achieved values of 79–82%. In [ 13 ], the rate of absence was discretized into just two classes: less than or equal to 5 hours, or greater than 5 hours. Dogruyol and Sekeroglu [ 14 ] and Araujo et al [ 15 ], on the other hand, treated the target variable—absenteeism in hours—as a continuous variable.…”

Deep learning support for intelligent transportation systems

“…In the industrial domain, deep learning and machine learning algorithms such as Random Forest, Deep Neural Network, Multilayer Feedforward Neural Network, t-Distributed Stochastic Neighbor Embedding (t-SNE), faster region-based convolutional neural networks, and region-based fully convolutional networks have been used to improve the safety of workers around heavy machinery by automatically detecting when people or objects are within an unsafe distance from machines or for risk detection and trajectory tracking at construction sites (we need to verify the identity of the workers and track their walking paths). [71][72][73] Construction worker detection has been proposed for construction safety, 74,75 worker behavior analysis 76,77 and productivity analysis. 78,79 The results obtained have shown a 98% prediction accuracy for work zone events, thus using prevent variables can be a viable proposal to predict the occurrence of a safety-critical event using those models.…”

Identification of important features and data mining classification techniques in predicting employee absenteeism at work

“…Absenteeism at work can be described as a "habitual pattern of absence from a duty or obligation" [1]. This happens when employees do not show up or engage in events related either directly or indirectly to their jobs [2].…”