Industrial Accident
Analysis and Predictive Models for Workplace Hazard Prevention
Rao Eswara Veera Raghava and Pandey Ashutosh
Disaster Advances; Vol. 18(6); 58-64;
doi: https://doi.org/10.25303/186da058064; (2025)
Abstract
Accidents are a constant problem in numerous enterprises and they greatly affect
workers and project results. This study aims to find out what caused these crashes,
focusing on how worker-related, environmental and managerial factors all interact
with each other. The goal is to find the main factors affecting Workplace Hazard
Prevention (WHP) and make a model that can predict the future to lower risks. This
study uses an ensemble machine learning (EML) approach to show Industrial Accident
Analysis and Predictive Models for Workplace Hazard Prevention (IAA-PM-WHP). An
analysis is conducted on a publicly accessible collection of 65,518 workplace injury
reports from the Occupational Safety and Health Administration (OSHA), using four
distinct ML models.
This study suggested a way to build a model that takes into account three important
factors: "type of damage," "kind of event," and "harmed organ." The EML model integrates
predictions from four fundamental ML methodologies via soft voting. Among classic
ML models, the RF method had the greatest accuracy (0.89), indicating robust overall
prediction power. The EML method outperformed all models, attaining the greatest
accuracy (0.92), precision (0.99), recall (0.899), F1-score (0.94) and AUC (0.92).
