Kaire: An intelligent model to identify and predict work-related stressors in industrial settings

Authors:
Publisher website
Google Scholar
Abstract
Mental health issues significantly affect workers and industries by reducing productivity, generating economic losses, and contributing to psycho-physical conditions. Technological advancements are transforming workplaces, emphasizing mental health care through digitalization and automation. This study introduces Kaire, an intelligent model designed to identify and predict work-related stressors in industrial settings. Kaire utilizes an ontology-based framework combined with semantic rules and machine learning algorithms, including Support Vector Machine (SVM) and Random Forest (RF), to classify stressors and forecast events. Synthetic data simulating industrial routines evaluated Kaire’s performance, with SVM achieving 99.36% accuracy, 99.55% precision, 98.84% recall, and 99.19% F1 score. The Group Stress Index (GSI) analysis showed 88.39% similarity between calculated results and simulator outputs. While event prediction scored lower across all evaluated configurations, stressor identification and GSI estimation showed consistent results in the simulated scenario. Kaire contributes to stress identification and monitoring, supporting industries in promoting workers’ mental health and well-being.

This publication has 40 references indexed in Scilit: