ObjectiveTo construct and evaluate various machine learning models for predicting the risk of chronic obstructive pulmonary disease （COPD） in individuals， thereby providing data support for early screening and intervention. MethodsA total of 823 subjects were selected for this study， comprising 142 individuals in the high-risk group for COPD and 681 individuals in the low-risk group. Data collected included demographic characteristics， smoking history， symptoms （such as cough and shortness of breath）， and scores from the Chronic obstructive pulmonary disease screening questionnaire. Four machine learning algorithms—Logistic regression， random forest， support vector machine， and XGBoost—were utilized to construct risk prediction models. The performance of these models was assessed using 5-fold cross-validation， with evaluation metrics including accuracy， precision， recall， F1-score， area under the receiver operating characteristic curve （AUC）， and average precision （AP）. Furthermore， a feature importance analysis was performed. ResultsThe Logistic Regression model exhibited superior performance， achieving an AUC of 0.982 and an AP of 0.939. This was closely followed by the Random Forest model， which recorded an AUC of 0.975 and an AP of 0.890. Feature importance analysis revealed that smoking history， symptoms of shortness of breath， and body weight were significant predictors. All models demonstrated robust performance in identifying low-risk populations； however， variations were observed in their efficacy in identifying high-risk populations. ConclusionMachine learning models have proven effective in identifying individuals at high risk for COPD. Among these， the Logistic regression model exhibits the best overall performance， efficiently identifying high-risk populations and serving as a valuable clinical auxiliary screening tool. Various models， each with distinct performance characteristics， are suited to different clinical screening scenarios， thereby offering targeted decision-making support for the establishment of a hierarchical and intelligent COPD screening pathway.

Objective:To understand the occupational health status of a lead-acid battery enterprise in Jiangsu Province, to observe the results of blood lead and bone mineral density (BMD) of the workers exposed to occupational lead, and to explore the effect of occupational lead exposure on BMD, so as to provide basis for the prevention and treatment of occupational lead poisoning and osteoporosis.Methods:An occupational health survey was conducted in a lead-acid battery enterprise in Jiangsu Province in January 2019. Basic information and occupational health examination results of 402 persons exposed to occupational lead were collected, and BMD was measured. Spearman rank correlation test was used to analyze the relationship between blood lead and BMD, and logistic regression analysis was used to analyze the influencing factors of BMD.Results:The blood lead level M ( P25, P75) of 402 occupational lead exposure workers was 220.5 (118.0, 307.0) μg/L, 46 workers (11.4%) had blood lead value ≥400 μg/L, and 5 workers (1.2%) ≥600 μg/L. 124 workers (30.8%) had abnormal BMD. The concentrations of lead dust and lead smoke in the workplace were <0.004-0.027 and <0.021-0.045 mg/m 3, respectively. The positions exceeding the standard point were mainly concentrated in the casting and welding group (44.4%, 4/9) of lead smoke positions. There was a statistically significant difference in the overall distribution of blood lead levels among lead exposure workers with different BMD levels, and there was a positive correlation between blood lead and BMD ( P<0.01) . The results of univariate analysis showed that there were statistically significant differences in the distribution of abnormal BMD among workers exposed to different genders, positions and blood lead levels ( P<0.01) . The results of multivariate logistic regression analysis showed that the risk of abnormal BMD in male workers was 5.069 times of that in female worker (95% CI: 2.906-8.840, P<0.01) . Conclusion:Occupational lead exposure personnel have a high blood lead level and a high abnormal BMD rate. Exposure to lead working environment is an influencing factor for the abnormal BMD of workers, so enterprise managers should pay attention to health protection, occupational health monitoring and supervision of working environment of front-line workers.

Objective:To understand the occupational health status of a lead-acid battery enterprise in Jiangsu Province, to observe the results of blood lead and bone mineral density (BMD) of the workers exposed to occupational lead, and to explore the effect of occupational lead exposure on BMD, so as to provide basis for the prevention and treatment of occupational lead poisoning and osteoporosis.Methods:An occupational health survey was conducted in a lead-acid battery enterprise in Jiangsu Province in January 2019. Basic information and occupational health examination results of 402 persons exposed to occupational lead were collected, and BMD was measured. Spearman rank correlation test was used to analyze the relationship between blood lead and BMD, and logistic regression analysis was used to analyze the influencing factors of BMD.Results:The blood lead level M ( P25, P75) of 402 occupational lead exposure workers was 220.5 (118.0, 307.0) μg/L, 46 workers (11.4%) had blood lead value ≥400 μg/L, and 5 workers (1.2%) ≥600 μg/L. 124 workers (30.8%) had abnormal BMD. The concentrations of lead dust and lead smoke in the workplace were <0.004-0.027 and <0.021-0.045 mg/m 3, respectively. The positions exceeding the standard point were mainly concentrated in the casting and welding group (44.4%, 4/9) of lead smoke positions. There was a statistically significant difference in the overall distribution of blood lead levels among lead exposure workers with different BMD levels, and there was a positive correlation between blood lead and BMD ( P<0.01) . The results of univariate analysis showed that there were statistically significant differences in the distribution of abnormal BMD among workers exposed to different genders, positions and blood lead levels ( P<0.01) . The results of multivariate logistic regression analysis showed that the risk of abnormal BMD in male workers was 5.069 times of that in female worker (95% CI: 2.906-8.840, P<0.01) . Conclusion:Occupational lead exposure personnel have a high blood lead level and a high abnormal BMD rate. Exposure to lead working environment is an influencing factor for the abnormal BMD of workers, so enterprise managers should pay attention to health protection, occupational health monitoring and supervision of working environment of front-line workers.