# Prediction of Workplace Injuries

## TL;DR

This paper presents a comprehensive approach to predicting workplace injuries by addressing data imbalance, transferring knowledge across organizations, and uncovering causal factors to improve injury prevention strategies.

## Contribution

It introduces ensemble resampling methods, a novel transfer learning approach, and techniques for causal analysis in injury risk prediction, advancing the field significantly.

## Key findings

- Ensemble resampling improves prediction accuracy on imbalanced datasets.
- Transfer learning effectively generalizes injury risk models across organizations.
- Causal analysis identifies key variables influencing injury risk.

## Abstract

Workplace injuries result in substantial human and financial losses. As reported by the International Labour Organization (ILO), there are more than 374 million work-related injuries reported every year. In this study, we investigate the problem of injury risk prediction and prevention in a work environment. While injuries represent a significant number across all organizations, they are rare events within a single organization. Hence, collecting a sufficiently large dataset from a single organization is extremely difficult. In addition, the collected datasets are often highly imbalanced which increases the problem difficulty. Finally, risk predictions need to provide additional context for injuries to be prevented. We propose and evaluate the following for a complete solution: 1) several ensemble-based resampling methods to address the class imbalance issues, 2) a novel transfer learning approach to transfer the knowledge across organizations, and 3) various techniques to uncover the association and causal effect of different variables on injury risk, while controlling for relevant confounding factors.