# Occupational Risk Prediction for Miners Based on Stacking Health Data Fusion
###### Abstract  
Occupational health risk prediction of miners is a core issue to ensure the safety of high-risk operations. Current risk assessment methodologies face critical limitations, as conventional unimodal prediction systems frequently demonstrate limited efficacy in capturing the multifactorial nature of occupational health deterioration. This study presents a novel stacked ensemble architecture employing dual-phase algorithmic optimization to address these muti-parametric interactions. The proposed framework implements a hierarchical modeling paradigm: (1) a primary predictive layer employing heterogeneous base learners (Random Forest and Logistic Regression classifiers) to establish foundational decision boundaries, and (2) a meta-modeling stratum utilizing regularized logistic regression with hyperparameter optimization via grid search-assisted k-fold cross-validation. Empirical validation through comparative analysis reveals the enhanced ensemble achieves a mean accuracy of 90%. Receiver operating characteristic analysis confirms superior discriminative capacity (AUC = 0.89), surpassing conventional ensemble methods by 23.3 percentile points. The model's capacity to quantify nonlinear exposure-response relationships while maintaining computational tractability suggests significant utility in occupational health surveillance systems. These findings substantiate that the proposed dual-layer optimization framework substantially advances predictive capabilities in occupational health epidemiology, particularly in addressing the complex synergies between environmental hazards and physiological responses in confined industrial environments. m mlevels of dust, extreme temperatures, and high humidity, which have significantly impacted on their health, leading to a marked increase in health-related issues among miners [2]. Miners, as high-risk workers, are exposed to harsh environments that pose significant threats to their lives. Traditional safety monitoring methods primarily focus on environmental and operational conditions but often neglect the real-time monitoring of human physiological states. This oversight results in inadequate early warnings of potential hazards. In recent years, although many researchers have used sensors to monitor human vital signs, these efforts have primarily focused on disease diagnosis and the exploration of pathogenic factors [3]. Additionally, numerous experts have focused on the impact of environmental factors on miners, such as evaluating gas concentrations and assessing safe zones to identify potentially hazardous areas [4]. However, few studies analyze vital sign data in relation to workplace safety risks. Accurately assessing the health status and safety risks of personnel remains a significant challenge. [PERSON] et al. [5] analyzed mining accidents in China and concluded that unsafe behaviors were the primary factors influencing safety. [PERSON] et al. [6] analyzed mining accidents from 2013 to 2023. The majority of accidents were caused by unsafe behaviors of personnel, accounting for 74%. The second most common cause included machine states and environmental factors. [PERSON] et al. [7] demonstrated that addressing unsafe individual states significantly reduces the likelihood of unsafe behaviors among miners. A total of 349 safety incidents occurred between 2021 and 2023, including 89 incidents with 191 casualties in 2021, 139 incidents with 215 casualties in 2022, and 121 incidents with 281 casualties in 2023. Figure 1 shows the accident statistics in China from 2021 to 2023. Serious accidents are often caused by multiple factors, with unsafe personnel behavior being a key contributor. By predicting and assessing personnel insecurity factors, our system can reduce the likelihood of accidents at the source.