{"title":"分析职业暴露对男性生育指标的影响:一种机器学习方法。","authors":"Hamzeh Mohammadi , Shayan Khoddam , Farideh Golbabaei , Somayeh Farhang Dehghan","doi":"10.1016/j.reprotox.2025.108959","DOIUrl":null,"url":null,"abstract":"<div><div>Occupational exposures are critical factors affecting workers' reproductive health. This study investigates the impact of magnetic fields, electric fields, whole-body vibration, noise levels, and heat stress on male reproductive indicators using advanced machine learning models. The aim is to identify key risk factors and provide predictive insights into workers' reproductive health over the next decade. Data were collected from 80 male workers in an automobile part manufacturing plant, capturing demographic characteristics, occupational exposures, biochemical markers, hormone levels, and sperm parameters. Five machine learning models logistic regression, bagging classifier, extreme gradient boosting, random forest, and support vector machine were trained and evaluated using 5-fold cross-validation to determine effective predictors of reproductive health outcomes. Exposure to whole-body vibration, magnetic fields, electric fields, and heat stress closely affected free testosterone levels, with SHAP importance indicating: Magnetic Field Exposure (0.339) and Wet Bulb Globe Temperature (0.138). Worker Age (0.244) was the most influential demographic factor negatively impacting Free Testosterone. The XGBoost and random forest achieved the highest AUC (0.99), outperforming other models in predictive accuracy. The Random Forest model Importance (% Increase in MSE) predicted that Electric Field Exposure (5 %) and Magnetic Field Exposure (4.7 %) would have the most substantial negative impact on Free Testosterone, followed by Worker Age (4.1 %). This study underscores the need for targeted interventions, such as improved workplace safety protocols and regular health monitoring, to protect workers’ reproductive health.</div></div>","PeriodicalId":21137,"journal":{"name":"Reproductive toxicology","volume":"136 ","pages":"Article 108959"},"PeriodicalIF":2.8000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analyzing the impact of occupational exposures on male fertility indicators: A machine learning approach\",\"authors\":\"Hamzeh Mohammadi , Shayan Khoddam , Farideh Golbabaei , Somayeh Farhang Dehghan\",\"doi\":\"10.1016/j.reprotox.2025.108959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Occupational exposures are critical factors affecting workers' reproductive health. This study investigates the impact of magnetic fields, electric fields, whole-body vibration, noise levels, and heat stress on male reproductive indicators using advanced machine learning models. The aim is to identify key risk factors and provide predictive insights into workers' reproductive health over the next decade. Data were collected from 80 male workers in an automobile part manufacturing plant, capturing demographic characteristics, occupational exposures, biochemical markers, hormone levels, and sperm parameters. Five machine learning models logistic regression, bagging classifier, extreme gradient boosting, random forest, and support vector machine were trained and evaluated using 5-fold cross-validation to determine effective predictors of reproductive health outcomes. Exposure to whole-body vibration, magnetic fields, electric fields, and heat stress closely affected free testosterone levels, with SHAP importance indicating: Magnetic Field Exposure (0.339) and Wet Bulb Globe Temperature (0.138). Worker Age (0.244) was the most influential demographic factor negatively impacting Free Testosterone. The XGBoost and random forest achieved the highest AUC (0.99), outperforming other models in predictive accuracy. The Random Forest model Importance (% Increase in MSE) predicted that Electric Field Exposure (5 %) and Magnetic Field Exposure (4.7 %) would have the most substantial negative impact on Free Testosterone, followed by Worker Age (4.1 %). This study underscores the need for targeted interventions, such as improved workplace safety protocols and regular health monitoring, to protect workers’ reproductive health.</div></div>\",\"PeriodicalId\":21137,\"journal\":{\"name\":\"Reproductive toxicology\",\"volume\":\"136 \",\"pages\":\"Article 108959\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Reproductive toxicology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0890623825001303\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"REPRODUCTIVE BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reproductive toxicology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0890623825001303","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"REPRODUCTIVE BIOLOGY","Score":null,"Total":0}
Analyzing the impact of occupational exposures on male fertility indicators: A machine learning approach
Occupational exposures are critical factors affecting workers' reproductive health. This study investigates the impact of magnetic fields, electric fields, whole-body vibration, noise levels, and heat stress on male reproductive indicators using advanced machine learning models. The aim is to identify key risk factors and provide predictive insights into workers' reproductive health over the next decade. Data were collected from 80 male workers in an automobile part manufacturing plant, capturing demographic characteristics, occupational exposures, biochemical markers, hormone levels, and sperm parameters. Five machine learning models logistic regression, bagging classifier, extreme gradient boosting, random forest, and support vector machine were trained and evaluated using 5-fold cross-validation to determine effective predictors of reproductive health outcomes. Exposure to whole-body vibration, magnetic fields, electric fields, and heat stress closely affected free testosterone levels, with SHAP importance indicating: Magnetic Field Exposure (0.339) and Wet Bulb Globe Temperature (0.138). Worker Age (0.244) was the most influential demographic factor negatively impacting Free Testosterone. The XGBoost and random forest achieved the highest AUC (0.99), outperforming other models in predictive accuracy. The Random Forest model Importance (% Increase in MSE) predicted that Electric Field Exposure (5 %) and Magnetic Field Exposure (4.7 %) would have the most substantial negative impact on Free Testosterone, followed by Worker Age (4.1 %). This study underscores the need for targeted interventions, such as improved workplace safety protocols and regular health monitoring, to protect workers’ reproductive health.
期刊介绍:
Drawing from a large number of disciplines, Reproductive Toxicology publishes timely, original research on the influence of chemical and physical agents on reproduction. Written by and for obstetricians, pediatricians, embryologists, teratologists, geneticists, toxicologists, andrologists, and others interested in detecting potential reproductive hazards, the journal is a forum for communication among researchers and practitioners. Articles focus on the application of in vitro, animal and clinical research to the practice of clinical medicine.
All aspects of reproduction are within the scope of Reproductive Toxicology, including the formation and maturation of male and female gametes, sexual function, the events surrounding the fusion of gametes and the development of the fertilized ovum, nourishment and transport of the conceptus within the genital tract, implantation, embryogenesis, intrauterine growth, placentation and placental function, parturition, lactation and neonatal survival. Adverse reproductive effects in males will be considered as significant as adverse effects occurring in females. To provide a balanced presentation of approaches, equal emphasis will be given to clinical and animal or in vitro work. Typical end points that will be studied by contributors include infertility, sexual dysfunction, spontaneous abortion, malformations, abnormal histogenesis, stillbirth, intrauterine growth retardation, prematurity, behavioral abnormalities, and perinatal mortality.