Article(id=1282032500088214150, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.09.1075, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1745596800000, receivedDateStr=2025-04-26, revisedDate=1752249600000, revisedDateStr=2025-07-12, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589967033, onlineDateStr=2026-07-09, pubDate=1758988800000, pubDateStr=2025-09-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589967033, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589967033, creator=13701087609, updateTime=1783589967033, updator=13701087609, issue=Issue{id=1282032292440805784, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='9', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1758988800000', pubDateStr='2025-09-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589917526, creator='13701087609', updateTime=1783593837820, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048735404344166, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048735404344167, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282032292440805784, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=253, endPage=262, ext={EN=ArticleExt(id=1282032502185366151, articleId=1282032500088214150, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Noise risk classification of coal mine occupational health based on approximate Markov blanket and GBDT, columnId=1149735805633081985, journalTitle=China Safety Science Journal, columnName=Occupational health, runingTitle=null, highlight=null, articleAbstract=

To accurately assess the impact of noise on the health of workers in fully mechanized mining face, the key influencing factors of noise occupational health were determined by using the theory of man-machine-environment-management system, combined with Fisher Score, maximum information coefficient and approximate Markov blanket method. The prediction model of coal mine noise risk classification based on the GBDT algorithm was constructed, and the Kappa coefficient and its accuracy were used as the index of model efficiency to compare and verify the accuracy of the model. The results show that the occupational health damage of noise in fully mechanized mining face is closely related to individual status, equipment configuration, environmental factors and occupational health management. Among these, job category, individual age, length of service, protection awareness, degree of equipment automation, pass rate of noise monitoring points, noise exposure, reverberation time and management institutions and personnel are the key indicators of occupational health risk classification and prediction. The accuracy of the occupation health risk classification prediction model of coal mine noise based on GBDT is up to 99.6%, and the average accuracy and Kappa coefficient are 98.3% and 0.958, respectively. The evaluation accuracy of six prediction models for noise occupational health risk classification in fully mechanized mining face is ranked as follows: GBDT > Genetic Algorithm optimization Random Forest(GA-RF) > Particle Swarm optimization Least Squares Support Vector Machine(PSO-LSSVM) > Random Forest(RF) > Support Vector Machine(SVM) > Decision tree.

, authors=Xiaoxu GAO^{1, 2}, Jiake TIAN^{**, 1}, Lu GAO³, Lu DU¹, Mengjie FAN¹, authorsList=Xiaoxu GAO, Jiake TIAN, Lu GAO, Lu DU, Mengjie FAN, authorCompany=null, correspAuthors=Jiake TIAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1282032510313927306, articleId=1282032500088214150, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于近似马尔可夫毯与GBDT的煤矿职业健康噪声风险分类, columnId=1149735805817631364, journalTitle=中国安全科学学报, columnName=职业卫生, runingTitle=null, highlight=null, articleAbstract=

为精准判断综采工作面噪声对作业人员的健康损害程度，采用人-机-环-管系统理论结合Fisher Score、最大信息系数及近似马尔可夫毯方法，确定噪声职业健康的关键影响因素；构建基于梯度提升决策树（GBDT）算法的煤矿噪声风险分类预测模型，并以Kappa系数及其准确率作为模型效率的指标，来对比验证本算法模型的准确性。结果表明：综采工作面噪声职业健康损害情况与个体状况、设备配置、环境因素及职业健康管理等因素密切相关，其中，岗位类别、个体年龄、工龄时长、防护意识、设备自动化程度、噪声监测点合格率、噪声暴露量、混响时间和管理机构及人员为职业健康风险分类预测的关键指标；基于GBDT构建的煤矿噪声职业健康风险分类预测模型准确率最大达99.6%，平均准确率和Kappa系数分别为98.3%和0.958；计算确定6种综采工作面噪声职业健康风险分类预测模型评估准确率次序为：GBDT >遗传算法优化随机森林算法（GA-RF）>粒子群算法优化最小二乘支持向量机算法（PSO-LSSVM）>随机森林算法（RF）>支持向量机算法（SVM）>决策树。

, authors=高晓旭教授^{1, 2}, 田佳可^{**, 1}, 高璐³, 杜芦¹, 范萌杰¹, authorsList=高晓旭教授, 田佳可, 高璐, 杜芦, 范萌杰, authorCompany=null, correspAuthors=田佳可, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=vwh+eHara3s0JILe1Kff/g==, magXml=mCVJYEaQZ5LKIO212xlUaQ==, pdfUrl=null, pdf=FUhFO4GcI3RVXbKe6yJivA==, pdfFileSize=8700260, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=ESZ2lDsOLJmqlC/tjx6dpQ==, mapNumber=null, fund=null)}, authors=[Author(id=1282032519071634069, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282032500088214150, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=396783933@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282032521097482904, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282032500088214150, authorId=1282032519071634069, language=EN, stringName=Xiaoxu GAO, firstName=Xiaoxu, middleName=null, lastName=GAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹College of Energy and Mining Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
²Key Laboratory of Western Mine Exploration and Hazard Prevention Under Ministry of Education, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282032521592410777, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282032500088214150, authorId=1282032519071634069, language=CN, stringName=高晓旭教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹西安科技大学 能源与矿业工程学院，陕西 西安 710054
²西安科技大学 西部矿井开采及灾害防治教育部重点实验室，陕西 西安 710054, bio={"content":"

高晓旭（1978—），男，山西吕梁人，博士，教授，硕士生导师，主要从事采矿安全与职业健康等方面的研究。E-mail: 396783933@qq.com。

"}, bioImg=null, bioContent=

高晓旭（1978—），男，山西吕梁人，博士，教授，硕士生导师，主要从事采矿安全与职业健康等方面的研究。E-mail: 396783933@qq.com。

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