Abstract
The prevention of occupational accidents in high-risk industrial environments requires solutions capable of anticipating unsafe conditions in real time. The objective of this research was to develop a predictive model based on artificial intelligence and smart sensors for the dynamic prevention of occupational accidents. The methodology integrated Industrial Internet of Things (IIoT) technologies, machine learning algorithms, Monte Carlo simulation, and explainable artificial intelligence to analyze environmental, operational, and human variables. The results showed that the XGBoost model achieved an accuracy of 94% and an AUC of 0.97, while the SHAP analysis identified operational fatigue, abnormal vibrations, and ambient temperature as the main risk-related factors. It is concluded that the integration of smart sensors, artificial intelligence, and advanced simulation strengthens industrial safety through predictive systems aimed at early risk detection and preventive decision-making.
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