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Explainable Ensemble Machine Learning for Predicting Injury Severity in Agricultural Accidents.

Omer Mermer1, Eddie Zhang2, Ibrahim Demir1,3

  • 1By Water Institute, Tulane University, New Orleans, LA, USA.

Journal of Agromedicine
|April 24, 2026
PubMed
Summary
This summary is machine-generated.

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Machine learning models accurately predict agricultural injury severity. Explainable AI identified age, gender, location, and time as key risk factors for farm injuries.

Area of Science:

  • Agricultural Safety
  • Occupational Health
  • Machine Learning Applications

Background:

  • Agricultural injuries pose significant global risks, impacting human well-being and economic stability.
  • Predictive modeling can enhance understanding and mitigation of these hazards.

Purpose of the Study:

  • To predict agricultural injury severity using machine learning (ML) models.
  • To ensure model interpretability via explainable artificial intelligence (XAI).

Main Methods:

  • Analysis of 2,421 agricultural incidents (2015-2024) from AgInjuryNews.
  • Implementation and evaluation of various ML and ensemble models (e.g., Random Forest, XGBoost).
  • Application of Shapley Additive Explanations (SHAP) for predictor identification.
Keywords:
Agricultural injurySHAP analysisensemble modelsexplainable AI (XAI)injury severity predictionmachine learning

Related Experiment Videos

Main Results:

  • Ensemble models, particularly XGBoost, demonstrated superior performance in predicting injury severity.
  • XGBoost achieved high recall for fatal injuries, though non-fatal injury classification faced challenges due to data imbalance.
  • SHAP analysis identified age, gender, location, and time as critical predictors.

Conclusions:

  • Ensemble ML and XAI provide effective tools for predicting agricultural injury severity and identifying risk factors.
  • Addressing data imbalance is crucial for improving non-fatal injury prediction.
  • Findings can inform targeted safety interventions and policy development to reduce agricultural injuries.