Logo image
Back
Agricultural Injury Severity Prediction Using Integrated Data-Driven Analysis: Global Versus Local Explainability Using SHAP
Journal article   Open access   Peer reviewed

Agricultural Injury Severity Prediction Using Integrated Data-Driven Analysis: Global Versus Local Explainability Using SHAP

Omer Mermer, Yanan Liu, Charles A. Jennissen, Milan Sonka and Ibrahim Demir
Safety (Basel), Vol.12(1), 6
01/08/2026
DOI: 10.3390/safety12010006
url
https://doi.org/10.3390/safety12010006View
Published (Version of record) Open Access

Abstract

Despite the agricultural sector’s consistently high injury rates, formal reporting is often limited, leading to sparse national datasets that hinder effective safety interventions. To address this, our study introduces a comprehensive framework leveraging advanced ensemble machine learning (ML) models to predict and interpret the severity of agricultural injuries. We use a unique, manually curated dataset of over 2400 agricultural incidents from AgInjuryNews, a public repository of news reports detailing incidents across the United States. We evaluated six ensemble models, including Gradient Boosting (GB), eXtreme Grading Boosting (XGB), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Histogram-based Gradient Boosting Regression Trees (HistGBRT), and Random Forest (RF), for their accuracy in classifying injury outcomes as fatal or non-fatal. A key contribution of our work is the novel integration of explainable artificial intelligence (XAI), specifically SHapley Additive exPlanations (SHAP), to overcome the “black-box” nature of complex ensemble models. The models demonstrated strong predictive performance, with most achieving an accuracy of approximately 0.71 and an F1-score of 0.81. Through global SHAP analysis, we identified key factors influencing injury severity across the dataset, such as the presence of helmet use, victim age, and the type of injury agent. Additionally, our application of local SHAP analysis revealed how specific variables like location and the victim’s role can have varying impacts depending on the context of the incident. These findings provide actionable, context-aware insights for developing targeted policy and safety interventions for a range of stakeholders, from first responders to policymakers, offering a powerful tool for a more proactive approach to agricultural safety.
 agriculture accident ensemble model explainable artificial intelligence (XAI) injury severity prediction machine learning (ML) occupation health and safety

Details

Metrics

8 Record Views
Logo image