Analyzing the impact of occupational exposures on male fertility indicators: A machine learning approach

IF 2.8 4区 医学 Q2 REPRODUCTIVE BIOLOGY
Hamzeh Mohammadi , Shayan Khoddam , Farideh Golbabaei , Somayeh Farhang Dehghan
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Abstract

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.
分析职业暴露对男性生育指标的影响:一种机器学习方法。
职业暴露是影响工人生殖健康的关键因素。本研究利用先进的机器学习模型研究了磁场、电场、全身振动、噪音水平和热应力对男性生殖指标的影响。其目的是确定关键的风险因素,并为未来十年工人的生殖健康提供预测性见解。数据收集自一家汽车零部件制造厂的80名男性工人,包括人口统计学特征、职业暴露、生化指标、激素水平和精子参数。通过5倍交叉验证,对逻辑回归、套袋分类器、极端梯度增强、随机森林和支持向量机等5个机器学习模型进行了训练和评估,以确定生殖健康结果的有效预测因子。暴露于全身振动、磁场、电场和热应激密切影响游离睾酮水平,与SHAP重要性表明:磁场暴露(0.339)和湿球温度(0.138)。工人年龄(0.244)是影响游离睾酮水平的最主要人口因素。XGBoost和随机森林获得了最高的AUC(0.99),在预测精度方面优于其他模型。随机森林模型重要性(MSE增加%)预测电场暴露(5%)和磁场暴露(4.7%)对游离睾酮的负面影响最大,其次是工人年龄(4.1%)。这项研究强调需要采取有针对性的干预措施,例如改进工作场所安全协议和定期健康监测,以保护工人的生殖健康。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Reproductive toxicology
Reproductive toxicology 生物-毒理学
CiteScore
6.50
自引率
3.00%
发文量
131
审稿时长
45 days
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.
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