Interpretability with Relevance Aggregation in Neural Networks for Absenteeism Prediction

The lack of attendance of employees is called absenteeism and occurs for various reasons, such as vigorous physical activity, advanced age and high psychological demands of the work. The absenteeism affects the direct and indirect costs of the companies, and may reach 15% of the payroll. Therefore, it is fundamental to know its main causes and contribute to control and mitigation strategies. Neural networks have been successfully applied in the classification of several problems, but they are black boxes, because they do not explain which aspects are considered in their decisions. This aspect is very important in health applications, in which it is necessary to explain and clearly interpret the results. In this context, this work presents an approach to classify absenteeism through neural networks and Layer-wise relevance propagation (LRP) aggregation in order to identify the most relevant features and to assign relevance scores individually per class and among all classes. The proposed approach was assessed by considering a dataset widely used as a benchmark and compared to the existing literature methods. The proposed approach presented the highest assertiveness rates among the compared methods, reaching an average accuracy of 0.83, identifying the most relevant features for the classification of absenteeism through a relevance score. Therefore, the results allow the interpretability of the causes of each class of absenteeism, which contribute to the management of human resources, occupational medicine and the development of strategies for its mitigation.