ArticleScientific reports2025
SHAP-driven insights into multimodal data: behavior phase prediction for industrial safety applications.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Machine learning model for multi-factor risk prediction in airport construction under non-stop operations.Scientific reports · 2026Article
- From narrative to machine-readable logic: Formalising and validating local indigenous knowledge for rural early warning systems.Jamba (Potchefstroom, South Africa) · 2026Article
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Authors and funding
7 authors.
Funding
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 complex coal mining scenarios. Key features influencing behavior prediction are identified using SHAP (Shapley Additive Explanations) analysis, including the total power of the heart rate variability spectrum (TP/ms
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Registered trials
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