SHAP-driven insights into multimodal data: behavior phase prediction for industrial safety applications.

ABSTRACT: Unsafe behaviors among coal miners are a primary factor contributing to accidents, posing significant challenges for safety management. This study develops a behavior state prediction framework using artificial intelligence and machine learning (ML) to investigate the relationship between workers' behavioral states and physiological characteristics. The framework employs AI-driven data analysis to support early warning systems and real-time interventions, enhancing coal mine safety protocols. Eight ML algorithms, including K-Nearest Neighbor (KNN), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), are evaluated. XGBoost achieves the highest performance, with 97.78% accuracy, 98.25% recall, and 97.86% F1-score, demonstrating strong generalization in comple

SUBMITTER: Li X 

PROVIDER: S-EPMC12504718 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature