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Article(id=1240972419310080147, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240972413354176744, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202312018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1701360000000, receivedDateStr=2023-12-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773800480915, onlineDateStr=2026-03-18, pubDate=1715270400000, pubDateStr=2024-05-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773800480915, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773800480915, creator=13701087609, updateTime=1773800480915, updator=13701087609, issue=Issue{id=1240972413354176744, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='9', pageStart='1537', pageEnd='1728', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773800479495, creator=13701087609, updateTime=1773800596829, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240972905568334240, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240972413354176744, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240972905568334241, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240972413354176744, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1677, endPage=1683, ext={EN=ArticleExt(id=1240972419599487128, articleId=1240972419310080147, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Establishment and verification of early screening model of chronic obstructive pulmonary disease based on three machine learning methods, columnId=1228016572451718132, journalTitle=Modern Preventive Medicine, columnName=Health and Social Behavior, runingTitle=null, highlight=null, articleAbstract=
Objective

To establish a screening model for patients with chronic obstructive pulmonary disease (COPD).

Methods

By using the method of multi-stage stratified random sampling, 4 587 permanent residents ≥ 40 years old in Guizhou Province were investigated by questionnaire, physical examination, and pulmonary function examination. Variables to be included into the model were screened by univariate analysis and then further screened by multivariate Logistic regression. Logistic regression (LR), random forest (RF) and support vector machine (SVM) were used to construct the screening model of COPD patients, and the area under the curve (AUC) was used to evaluate the effect of the model. Delong method was used to test the difference of AUC between models.

Results

According to the results of multivariate Logistic regression analysis, age, frequent cough before 14 years old, asthma, daily smoking, cooking fuel and exhaust, and harmful gas exposure were included in LR, RF and SVM models. The AUC of the three model training sets were 73.64%, 87.14%, and 73.30%, respectively, and the AUC of the test set were 76.10%, 70.96%, and 76.08%, respectively, all of which had good screening results. The results of Delong method showed that the screening effects of the three models were different between the training set and the test set.

Conclusion

This study established an economical, rapid, and effective screening model for COPD patients through six simple variables such as age and asthma.

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目的

构建慢性阻塞性肺疾病（chronic obstructive pulmonary disease，COPD）患者筛检模型。

方法

采用多阶段分层随机抽样的方法，抽取贵州省≥40岁的常住居民4 587名，对其进行问卷调查、体格检查及肺功能检查。经过单因素分析初步筛选模型纳入变量，经多因素logistic回归确定最终纳入变量。分别应用logistic回归（logistic regression，LR）、随机森林（random forest，RF）、支持向量机（support vector machine，SVM）构建COPD患者筛检模型，使用受试者工作曲线下面积（area under the curve，AUC）评价模型效果。使用delong法检验模型之间AUC的差异。

结果

根据多因素logistic回归分析结果，本研究将年龄、14岁前经常咳嗽、哮喘、每日吸烟量（支）、烹饪燃料与排风、有害气体暴露6种因素纳入LR、RF、SVM模型。三种模型训练集AUC分别为73.64%、87.14%、73.30%，测试集AUC分别为76.10%、70.96%、76.08%，均具有较好的筛检效果。Delong法结果显示，三种模型的筛检效果在训练集与测试集均存在一定差异。

结论

本研究通过年龄、哮喘等6个简单变量建立经济、快捷且有效的COPD患者筛检模型。

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刘涛，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=POOpLUha4655CvZZ0nL7Yw==, magXml=mcMYqHFT1AEM05MYpCmkdg==, pdfUrl=null, pdf=rBD7dLNH9ikVNpAlR2+vyQ==, pdfFileSize=818422, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=nwACeTddR0qZ8wD2NjnKKg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=owABq4KwfbBtIUNqwRgx9w==, mapNumber=null, authorCompany=null, fund=null, authors=

母应姣（1999—），女，硕士在读，研究方向：慢性病预防与控制

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母应姣（1999—），女，硕士在读，研究方向：慢性病预防与控制

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母应姣（1999—），女，硕士在读，研究方向：慢性病预防与控制

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On the role of training data for SVM-Based microwave brain stroke detection and classification[J].Sensors, 2023, 23(4): 2031., articleTitle=On the role of training data for SVM-Based microwave brain stroke detection and classification, refAbstract=null)], funds=[Fund(id=1240986274828439944, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, awardId=黔科合支撑［2021］一般447, language=CN, fundingSource=基于医防融合的健康管理中心大数据平台研究与示范(黔科合支撑［2021］一般447), fundOrder=null, country=null), Fund(id=1240986274937491853, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, awardId=null, language=CN, fundingSource=贵州省2019年中央补助地方重大疾病防治项目, fundOrder=null, country=null), Fund(id=1240986275033960850, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, awardId=null, language=CN, fundingSource=贵州省卫生健康委省级重点建设学科项目, fundOrder=null, country=null)], companyList=[AuthorCompany(id=1240986263881307111, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, xref=1., ext=[AuthorCompanyExt(id=1240986263889695719, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, companyId=1240986263881307111, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Key Laboratory of Environmental Pollution and Disease Monitoring, Ministry of Education, School of Public Health and Health,Guizhou Medical University Guiyang, Guiyang 561113, China), AuthorCompanyExt(id=1240986263898084329, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, companyId=1240986263881307111, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113)]), AuthorCompany(id=1240986264271377395, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, xref=2., ext=[AuthorCompanyExt(id=1240986264279766005, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, companyId=1240986264271377395, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.贵州省疾病预防控制中心，贵州 贵阳 550004)])], figs=[ArticleFig(id=1240986271561077054, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=EN, label=Figure 1, caption=Inclusion and exclusion of research subjects, figureFileSmall=SupfVrJ5XjF2BixzCUcQxA==, figureFileBig=cdudFFDxWOkdiJhQUhP4lA==, tableContent=null), ArticleFig(id=1240986271888232774, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=CN, label=图1, caption=研究对象的纳入及排除流程图, figureFileSmall=SupfVrJ5XjF2BixzCUcQxA==, figureFileBig=cdudFFDxWOkdiJhQUhP4lA==, tableContent=null), ArticleFig(id=1240986272609653069, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=EN, label=Figure 2, caption=ROC curves of the training set of three models, figureFileSmall=rr/otCTl89qM3lf7AnJ61g==, figureFileBig=RkAbTfh8FFcCn3/V4GKGsQ==, tableContent=null), ArticleFig(id=1240986273079415123, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=CN, label=图2, caption=三种模型训练集的ROC曲线图, figureFileSmall=rr/otCTl89qM3lf7AnJ61g==, figureFileBig=RkAbTfh8FFcCn3/V4GKGsQ==, tableContent=null), ArticleFig(id=1240986273272353113, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=EN, label=Figure 3, caption=ROC curves of the test set of three models, figureFileSmall=LSOx3C9ZjCdXUuio604aDA==, figureFileBig=Dm579r1zve91Np0QfC4/kQ==, tableContent=null), ArticleFig(id=1240986273377210717, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=CN, label=图3, caption=三种模型测试集的ROC曲线图, figureFileSmall=LSOx3C9ZjCdXUuio604aDA==, figureFileBig=Dm579r1zve91Np0QfC4/kQ==, tableContent=null), ArticleFig(id=1240986273494651235, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=EN, label=Table 1, caption=

Comparison of COPD related factors among the population in Guizhou Province ［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量非COPD患者（n=4 186)COPD患者（n=401）总人群（n=4 587）χ2/t值P值
年龄（岁）55.76±9.4262.32±9.3656.33±9.609.523<0.001
性别40.813<0.001
女1 997(94.73)111(5.27)2 108
男2 189(88.30)290(11.70)2 479
民族1.5930.207
汉族3 131(91.52)290(8.48)3 421
少数民族1 055(90.48)111(9.52)1 166
教育程度0.1450.862
文盲/半文盲2 028(91.11)198(8.89)2 226
小学/初中1 797(90.99)178(9.01)1 975
高中及以上361(93.52)25(6.48)386
城乡11.0320.001
农村3 632(90.73)371(9.27)4 003
城市554(94.86)30(5.14)584
婚姻0.8060.446
单身56(90.32)6(9.68)62
已婚/同居3 719(91.47)347(8.53)4 066
离异/丧偶/分居411(89.54)48(10.46)459
职业2.8260.093
务农2 028(90.50)213(9.50)2 241
非务农2 158(91.99)188(8.01)2 346
BMI（kg/m2）24.41±3.4523.39±3.2424.32±3.454.029<0.001
相关疾病史
14岁前经常咳嗽22.482<0.001
否4 103(91.56)378(8.44)4 481
是83(78.30)23(21.70)106
14岁前因肺炎或支气管炎住院3.4190.065
否4 137(91.34)392(8.66)4 529
是49(84.48)9(15.52)58
15～17岁因肺炎或支气管炎住院11.9320.001
否4 164(91.36)394(8.64)4 558
是22(75.86)7(24.14)29
患哮喘39.383<0.001
否4 104(91.75)369(8.25)4 473
是82(71.93)32(28.07)114
患支气管扩张症11.379<0.001
否4 166(91.36)394(8.64)4 560
是20(74.07)7(25.93)27
患高血压11.7810.001
否3 368(91.90)297(8.10)3 665
是818(88.72)104(11.28)922
相关家族史
父母患哮喘3.9870.046
否3 886(91.33)369(8.67)4 255
是300(90.36)32(9.64)332
父母患COPD1.9870.159
否3 664(91.42)344(8.58)4 008
是522(90.16)57(9.84)579
父母患慢性肺源性心脏病0.0020.961
否4 108(91.33)390(8.67)4 498
是78(87.64)11(12.36)89
父母患支气管扩张症4.9830.026
否4 145(91.32)394(8.68)4 539
是41(85.42)7(14.58)48
相关症状
经常咳嗽12.431<0.001
否3 968(91.75)357(8.25)4 325
是218(83.21)44(16.79)262
起床咳嗽11.6420.001
否3 988(91.76)358(8.24)4 346
是198(82.16)43(17.84)241
晚上咳嗽5.8870.015
否4 008(91.59)368(8.41)4 376
是178(84.36)33(15.64)211
经常咳痰17.787<0.001
否3 731(92.08)321(7.92)4 052
是455(85.05)80(14.95)535
起床咳痰25.803<0.001
否3 847(91.95)337(8.05)4 184
是339(84.12)64(15.88)403
反复发作的喘息33.286<0.001
否3 977(92.00)346(8.00)4 323
是209(79.17)55(20.83)264
气短或呼吸困难32.29<0.001
否3 838(92.19)325(7.81)4 163
是348(82.08)76(17.92)424
相关危险因素
每日吸烟量（支）18.942<0.001
02 466(94.19)152(5.81)2 618
0.1～19.9835(87.16)123(12.84)958
20.0～39.9760(87.56)108(12.44)868
≥40125(87.41)18(12.59)143
14岁前每天和吸烟者生活1.3510.245
否1 533(91.74)138(8.26)1 671
是2 653(90.98)263(9.02)2 916
14岁后每天和吸烟者生活0.8870.346
否1 430(90.91)143(9.09)1 573
是2 756(91.44)258(8.56)3 014
接触烹饪油烟0.2680.605
否2 719(91.00)269(9.00)2 988
是1 467(91.74)132(8.26)1 599
烹饪燃料与排风8.883<0.001
清洁燃料且排风2 006(93.43)141(6.57)2 147
清洁燃料不排风1 420(90.56)148(9.44)1 568
污染燃料且排风381(86.59)59(13.41)440
污染燃料不排风379(87.73)53(12.27)432
粉尘暴露2.2520.134
否696(23.63)249(76.37)2 945
是1 490(90.74)152(9.26)1 642
有害气体暴露20.916<0.001
否2 989(92.31)249(7.69)3 238
是1 197(88.73)152(11.27)1 349
), ArticleFig(id=1240986273700172136, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=CN, label=表1, caption=

贵州省人群COPD相关因素比较［（），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量非COPD患者（n=4 186)COPD患者（n=401）总人群（n=4 587）χ2/t值P值
年龄（岁）55.76±9.4262.32±9.3656.33±9.609.523<0.001
性别40.813<0.001
女1 997(94.73)111(5.27)2 108
男2 189(88.30)290(11.70)2 479
民族1.5930.207
汉族3 131(91.52)290(8.48)3 421
少数民族1 055(90.48)111(9.52)1 166
教育程度0.1450.862
文盲/半文盲2 028(91.11)198(8.89)2 226
小学/初中1 797(90.99)178(9.01)1 975
高中及以上361(93.52)25(6.48)386
城乡11.0320.001
农村3 632(90.73)371(9.27)4 003
城市554(94.86)30(5.14)584
婚姻0.8060.446
单身56(90.32)6(9.68)62
已婚/同居3 719(91.47)347(8.53)4 066
离异/丧偶/分居411(89.54)48(10.46)459
职业2.8260.093
务农2 028(90.50)213(9.50)2 241
非务农2 158(91.99)188(8.01)2 346
BMI（kg/m2）24.41±3.4523.39±3.2424.32±3.454.029<0.001
相关疾病史
14岁前经常咳嗽22.482<0.001
否4 103(91.56)378(8.44)4 481
是83(78.30)23(21.70)106
14岁前因肺炎或支气管炎住院3.4190.065
否4 137(91.34)392(8.66)4 529
是49(84.48)9(15.52)58
15～17岁因肺炎或支气管炎住院11.9320.001
否4 164(91.36)394(8.64)4 558
是22(75.86)7(24.14)29
患哮喘39.383<0.001
否4 104(91.75)369(8.25)4 473
是82(71.93)32(28.07)114
患支气管扩张症11.379<0.001
否4 166(91.36)394(8.64)4 560
是20(74.07)7(25.93)27
患高血压11.7810.001
否3 368(91.90)297(8.10)3 665
是818(88.72)104(11.28)922
相关家族史
父母患哮喘3.9870.046
否3 886(91.33)369(8.67)4 255
是300(90.36)32(9.64)332
父母患COPD1.9870.159
否3 664(91.42)344(8.58)4 008
是522(90.16)57(9.84)579
父母患慢性肺源性心脏病0.0020.961
否4 108(91.33)390(8.67)4 498
是78(87.64)11(12.36)89
父母患支气管扩张症4.9830.026
否4 145(91.32)394(8.68)4 539
是41(85.42)7(14.58)48
相关症状
经常咳嗽12.431<0.001
否3 968(91.75)357(8.25)4 325
是218(83.21)44(16.79)262
起床咳嗽11.6420.001
否3 988(91.76)358(8.24)4 346
是198(82.16)43(17.84)241
晚上咳嗽5.8870.015
否4 008(91.59)368(8.41)4 376
是178(84.36)33(15.64)211
经常咳痰17.787<0.001
否3 731(92.08)321(7.92)4 052
是455(85.05)80(14.95)535
起床咳痰25.803<0.001
否3 847(91.95)337(8.05)4 184
是339(84.12)64(15.88)403
反复发作的喘息33.286<0.001
否3 977(92.00)346(8.00)4 323
是209(79.17)55(20.83)264
气短或呼吸困难32.29<0.001
否3 838(92.19)325(7.81)4 163
是348(82.08)76(17.92)424
相关危险因素
每日吸烟量（支）18.942<0.001
02 466(94.19)152(5.81)2 618
0.1～19.9835(87.16)123(12.84)958
20.0～39.9760(87.56)108(12.44)868
≥40125(87.41)18(12.59)143
14岁前每天和吸烟者生活1.3510.245
否1 533(91.74)138(8.26)1 671
是2 653(90.98)263(9.02)2 916
14岁后每天和吸烟者生活0.8870.346
否1 430(90.91)143(9.09)1 573
是2 756(91.44)258(8.56)3 014
接触烹饪油烟0.2680.605
否2 719(91.00)269(9.00)2 988
是1 467(91.74)132(8.26)1 599
烹饪燃料与排风8.883<0.001
清洁燃料且排风2 006(93.43)141(6.57)2 147
清洁燃料不排风1 420(90.56)148(9.44)1 568
污染燃料且排风381(86.59)59(13.41)440
污染燃料不排风379(87.73)53(12.27)432
粉尘暴露2.2520.134
否696(23.63)249(76.37)2 945
是1 490(90.74)152(9.26)1 642
有害气体暴露20.916<0.001
否2 989(92.31)249(7.69)3 238
是1 197(88.73)152(11.27)1 349
), ArticleFig(id=1240986273893110126, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=EN, label=Table 2, caption=

Results of multivariate logistic regression analysis for COPD

, figureFileSmall=null, figureFileBig=null, tableContent=
变量比较组参照组β t/Waldχ2值P值OR值(95%CI)
年龄0.0520.0086.881<0.0011.053(1.038～1.069)
性别男女0.4120.3081.3370.1811.510(0.825～2.764)
城乡城市农村-0.2610.212-1.2270.2200.771(0.508～1.169)
BMI-0.0290.022-1.3040.1920.972(0.930～1.015)
14岁前经常咳嗽是否0.8810.3212.7450.0062.413(1.286～4.526)
15～17岁因肺炎或支气管炎住院是否0.7570.5211.4530.1462.132(0.768～5.922)
患哮喘是否0.9750.3842.5420.0112.651(1.250～5.624)
患支气管扩张症是否0.5130.6740.7600.4471.670(0.445～6.264)
患高血压是否0.1200.1680.7180.4731.128(0.812～1.567)
父母患哮喘是否0.1380.2700.5120.6091.148(0.677～1.948)
父母患COPD是否0.3420.4780.7150.4751.407(0.551～3.593)
父母患支气管扩张症是否0.4330.3901.1100.2671.543(0.717～3.317)
经常咳嗽是否-0.3730.395-0.9450.3440.689(0.318～1.493)
起床咳嗽是否-0.4410.366-1.2040.2290.644(0.314～1.319)
晚上咳嗽是否-0.2000.240-0.8330.4050.819(0.511～1.311)
经常咳痰是否0.4850.3051.5890.1121.625(0.893～2.958)
起床咳痰是否0.3610.2491.4520.1461.435(0.881～2.336)
反复发作的喘息是否0.3280.2221.4770.1401.389(0.898～2.147)
气短或呼吸困难是否0.4410.3161.3950.1631.554(0.837～2.885)
每日吸烟量（支）0.1～19.900.6720.3012.2350.0251.957(1.086～3.528)
20.0～39.90.4410.4131.0690.2851.554(0.692～3.491)
≥400.2420.1601.5190.1291.274(0.932～1.742)
烹饪燃料与排风清洁燃料不排风清洁燃料且排风0.4760.2242.1270.0331.609(1.038～2.495)
污染燃料且排风0.1060.2210.4820.6301.112(0.722～1.715)
污染燃料不排风0.3100.1472.1150.0341.363(1.023～1.817)
有害气体暴露是否0.3060.1472.0790.0381.358(1.018～1.812）
), ArticleFig(id=1240986274002162036, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=CN, label=表2, caption=

COPD多因素logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量比较组参照组β t/Waldχ2值P值OR值(95%CI)
年龄0.0520.0086.881<0.0011.053(1.038～1.069)
性别男女0.4120.3081.3370.1811.510(0.825～2.764)
城乡城市农村-0.2610.212-1.2270.2200.771(0.508～1.169)
BMI-0.0290.022-1.3040.1920.972(0.930～1.015)
14岁前经常咳嗽是否0.8810.3212.7450.0062.413(1.286～4.526)
15～17岁因肺炎或支气管炎住院是否0.7570.5211.4530.1462.132(0.768～5.922)
患哮喘是否0.9750.3842.5420.0112.651(1.250～5.624)
患支气管扩张症是否0.5130.6740.7600.4471.670(0.445～6.264)
患高血压是否0.1200.1680.7180.4731.128(0.812～1.567)
父母患哮喘是否0.1380.2700.5120.6091.148(0.677～1.948)
父母患COPD是否0.3420.4780.7150.4751.407(0.551～3.593)
父母患支气管扩张症是否0.4330.3901.1100.2671.543(0.717～3.317)
经常咳嗽是否-0.3730.395-0.9450.3440.689(0.318～1.493)
起床咳嗽是否-0.4410.366-1.2040.2290.644(0.314～1.319)
晚上咳嗽是否-0.2000.240-0.8330.4050.819(0.511～1.311)
经常咳痰是否0.4850.3051.5890.1121.625(0.893～2.958)
起床咳痰是否0.3610.2491.4520.1461.435(0.881～2.336)
反复发作的喘息是否0.3280.2221.4770.1401.389(0.898～2.147)
气短或呼吸困难是否0.4410.3161.3950.1631.554(0.837～2.885)
每日吸烟量（支）0.1～19.900.6720.3012.2350.0251.957(1.086～3.528)
20.0～39.90.4410.4131.0690.2851.554(0.692～3.491)
≥400.2420.1601.5190.1291.274(0.932～1.742)
烹饪燃料与排风清洁燃料不排风清洁燃料且排风0.4760.2242.1270.0331.609(1.038～2.495)
污染燃料且排风0.1060.2210.4820.6301.112(0.722～1.715)
污染燃料不排风0.3100.1472.1150.0341.363(1.023～1.817)
有害气体暴露是否0.3060.1472.0790.0381.358(1.018～1.812）
), ArticleFig(id=1240986274115408248, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=EN, label=Table 3, caption=

The screening effectiveness of LR, RF, and SVM models(%)

, figureFileSmall=null, figureFileBig=null, tableContent=
数据集参数LRRFSVM
训练集AUC73.64(72.05,87.14(86.06,73.30(71.10,
(95%CI)75.23)88.22)74.91)
灵敏性61.7381.9964.59
特异性69.3770.8568.42
约登指数31.1052.8433.01
测试集AUC76.10(70.69,70.96(65.61,76.08(70.58,
(95%CI)81.50)76.31)81.59)
灵敏性70.5165.3973.08
特异性71.3166.1969.52
约登指数41.8231.5842.60
), ArticleFig(id=1240986274383843710, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972419310080147, language=CN, label=表3, caption=

LR、RF、SVM模型筛检效果（%）

, figureFileSmall=null, figureFileBig=null, tableContent=
数据集参数LRRFSVM
训练集AUC73.64(72.05,87.14(86.06,73.30(71.10,
(95%CI)75.23)88.22)74.91)
灵敏性61.7381.9964.59
特异性69.3770.8568.42
约登指数31.1052.8433.01
测试集AUC76.10(70.69,70.96(65.61,76.08(70.58,
(95%CI)81.50)76.31)81.59)
灵敏性70.5165.3973.08
特异性71.3166.1969.52
约登指数41.8231.5842.60
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基于三种机器学习方法的慢性阻塞性肺疾病人群早筛模型的建立与验证
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母应姣 1 , 王子云 1 , 苏旭 2 , 李凌 2 , 周婕 2 , 王艺颖 2 , 刘涛 1, 2
现代预防医学 | 健康与社会行为 2024,51(9): 1677-1683
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现代预防医学 | 健康与社会行为 2024, 51(9): 1677-1683
基于三种机器学习方法的慢性阻塞性肺疾病人群早筛模型的建立与验证
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母应姣1, 王子云1, 苏旭2, 李凌2, 周婕2, 王艺颖2, 刘涛1, 2
作者信息
  • 1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113
  • 2.贵州省疾病预防控制中心，贵州 贵阳 550004

    • 母应姣（1999—），女，硕士在读，研究方向：慢性病预防与控制

通讯作者:

刘涛，E-mail:
Establishment and verification of early screening model of chronic obstructive pulmonary disease based on three machine learning methods
Ying-jiao MU1, Zi-yun WANG1, Xu SU2, Ling LI2, Jie ZHOU2, Yi-ying WANG2, Tao LIU1, 2
Affiliations
  • Key Laboratory of Environmental Pollution and Disease Monitoring, Ministry of Education, School of Public Health and Health,Guizhou Medical University Guiyang, Guiyang 561113, China
出版时间: 2024-05-10 doi: 10.20043/j.cnki.MPM.202312018
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摘要
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目的

构建慢性阻塞性肺疾病（chronic obstructive pulmonary disease，COPD）患者筛检模型。

方法

采用多阶段分层随机抽样的方法，抽取贵州省≥40岁的常住居民4 587名，对其进行问卷调查、体格检查及肺功能检查。经过单因素分析初步筛选模型纳入变量，经多因素logistic回归确定最终纳入变量。分别应用logistic回归（logistic regression，LR）、随机森林（random forest，RF）、支持向量机（support vector machine，SVM）构建COPD患者筛检模型，使用受试者工作曲线下面积（area under the curve，AUC）评价模型效果。使用delong法检验模型之间AUC的差异。

结果

根据多因素logistic回归分析结果，本研究将年龄、14岁前经常咳嗽、哮喘、每日吸烟量（支）、烹饪燃料与排风、有害气体暴露6种因素纳入LR、RF、SVM模型。三种模型训练集AUC分别为73.64%、87.14%、73.30%，测试集AUC分别为76.10%、70.96%、76.08%，均具有较好的筛检效果。Delong法结果显示，三种模型的筛检效果在训练集与测试集均存在一定差异。

结论

本研究通过年龄、哮喘等6个简单变量建立经济、快捷且有效的COPD患者筛检模型。

Logistic回归  /  随机森林  /  支持向量机  /  慢性阻塞性肺疾病  /  筛检
Objective

To establish a screening model for patients with chronic obstructive pulmonary disease (COPD).

Methods

By using the method of multi-stage stratified random sampling, 4 587 permanent residents ≥ 40 years old in Guizhou Province were investigated by questionnaire, physical examination, and pulmonary function examination. Variables to be included into the model were screened by univariate analysis and then further screened by multivariate Logistic regression. Logistic regression (LR), random forest (RF) and support vector machine (SVM) were used to construct the screening model of COPD patients, and the area under the curve (AUC) was used to evaluate the effect of the model. Delong method was used to test the difference of AUC between models.

Results

According to the results of multivariate Logistic regression analysis, age, frequent cough before 14 years old, asthma, daily smoking, cooking fuel and exhaust, and harmful gas exposure were included in LR, RF and SVM models. The AUC of the three model training sets were 73.64%, 87.14%, and 73.30%, respectively, and the AUC of the test set were 76.10%, 70.96%, and 76.08%, respectively, all of which had good screening results. The results of Delong method showed that the screening effects of the three models were different between the training set and the test set.

Conclusion

This study established an economical, rapid, and effective screening model for COPD patients through six simple variables such as age and asthma.

Logistic regression  /  Random forest  /  Support vector machine  /  Chronic obstructive pulmonary disease  /  Screening
母应姣, 王子云, 苏旭, 李凌, 周婕, 王艺颖, 刘涛. 基于三种机器学习方法的慢性阻塞性肺疾病人群早筛模型的建立与验证. 现代预防医学, 2024 , 51 (9) : 1677 -1683 . DOI: 10.20043/j.cnki.MPM.202312018
Ying-jiao MU, Zi-yun WANG, Xu SU, Ling LI, Jie ZHOU, Yi-ying WANG, Tao LIU. Establishment and verification of early screening model of chronic obstructive pulmonary disease based on three machine learning methods[J]. Modern Preventive Medicine, 2024 , 51 (9) : 1677 -1683 . DOI: 10.20043/j.cnki.MPM.202312018
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慢性阻塞性肺疾病（chronic obstructive pulmonary disease，COPD）是严重危害我国人民健康的呼吸系统疾病之一，《中国死因监测数据集（2021）》表明因COPD死亡人数占主要呼吸系统疾病首位[1]。由于COPD在早期无明显症状，多数COPD患者并未及时得到发现，从而错过最佳治疗时间。目前COPD筛查常使用肺功能检测，但肺功能检测成本高、操作难，在基层医疗机构开展较为困难。吕学莉等[2]研究显示我国≥40岁人群中肺功能检测率仅为5.9%。因此学者开始建立模型[3-4]以筛检COPD患者，随后建议筛查COPD患者到上级医院确诊，以做好COPD患者的“早发现、早诊断、早治疗”。近年来，早筛模型得到良好发展，一些模型[5-6]纳入年龄、海拔、结核病史、吸烟、COPD家族史等多种变量，AUC为67.8%～96.70%。但应注意到，这些模型的变量较为复杂，且同时不同地区存在环境与气候、生活习惯、经济水平等方面的差异，故应探索适宜各地防制工作的筛检模型。因此，本研究基于2019—2020年贵州省COPD调查数据，尝试通过logistic回归（logistic regression，LR）、随机森林（random forest，RF）、支持向量机（support vector machine，SVM）三种模型建立变量少、操作简单且性能较好的COPD患者筛检工具，为COPD患者的“三早预防”提供指导。
1　对象与方法
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1.1　研究对象
于2019年10月14日—2020年5月9日，将贵州省的县区按照性别、城镇化水平进行分层，随机抽取9个县区，抽中的县区按照与人口规模成比例（probability proportionate to size sampling，PPS）的方法抽取3个街道/乡镇，使用PPS抽样在抽中的街道/乡镇中随机抽取2个村，每个村随机抽取1个组（>150户），每个组随机抽取100户家庭（含≥40岁居民），采用KISH表法在每个家庭抽取1名居民进行调查。本研究开始前经中国疾控中心慢病中心伦理审查委员会审查通过（编号：201901），所有研究对象均签署知情同意书。
纳入标准：调查前12个月在调查地区居住6个月以上且年龄≥40岁的常住居民。
排除标准：（1）居住在功能区中的居民，如工棚、军队、学生宿舍、养老院等；（2）精神疾患或认知障碍，如痴呆、理解能力障碍、聋哑等；（3）新近发现和正在治疗的肿瘤；（4）高位截瘫；（5）妊娠期或哺乳期女性人群。
1.2　调查内容
调查内容由问卷调查、体格检查及肺功能检查组成。问卷调查使用《中国居民慢性阻塞性肺疾病监测》问卷[7]，由经过贵州省疾病预防控制中心及区（县）疾控中心培训合格的调查员使用电子平板面对面询问调查对象基本人口学信息、个人及家族史、呼吸道症状、吸烟情况、烹饪燃料等内容。在安静、平整的房间测量身高、体重等体格检查。使用德国耶格公司的便携式肺功能仪进行肺功能检测，内容包含第一秒用力呼气量（FEV1）、用力肺活量（FVC）、六秒用力呼气容积（FEV6）等。
1.3　相关定义
（1）COPD诊断：在支气管舒张试验后肺功能测试中，调查对象肺功能检测合格且FEV1/FVC<70%，即诊断为COPD[5]；（2）烹饪污染燃料：使用无烟煤等煤燃料、木头或动物粪便等生物燃料进行烹饪；（3）烹饪清洁燃料：指烹饪时使用液化气、燃气或电等清洁燃料；（4）烹饪排风：烹饪使用抽油烟机、排风扇或烟囱等排风装置；（5）有害气体：对身体有害的气体和蒸汽，如汽油、农药、油烟及二氧化硫等；（6）粉尘：工作环境中的灰尘、烟尘、烟雾、粉末、金属及化合物粉尘等；（7）体质指数（body mass index，BMI）：体重（kg）/身高的二次方（m2）；（8）肺功能测试不合格：肺功能检测时可接受操作≤1次，其中可接受操作是指呼吸迅速，起始无犹豫或有效的FEV6（用力时间>6 s，如呼气时间在<6 s，则要求其时间容量曲线须显示呼气相平台出现且超过2 s）。
1.4　统计学方法
本研究连续型资料的描述与组间比较分别采用（）和t检验，分类资料则采用频数（构成比）和χ2检验。将COPD可能相关因素进行t检验和χ2检验分析，具有统计学差异的变量进行多因素logistic回归分析筛选模型构建变量。为使结果对贵州省40岁及以上人群有代表性，统计学指标的计算均经复杂加权调整。
研究数据按照8:2分为训练集与测试集，使用ROSE包对训练集数据进行平衡。以是否患COPD（0=否，1=是）为结局变量，基于LR、RF、SVM建立筛检模型。LR由caret包的glm函数构建；RF由randomForest包的randomForest函数构建，使用bootstrap对样本进行重采样训练；SVM由e1071包的线性核函数构建。使用AUC、灵敏度、特异度等评价模型性能。使用delong法比较不同模型AUC差异。采用R(4.2.3)统计软件对所有资料进行统计学分析。采用双侧检验，检验水准α=0.05。
2　结果
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2.1　基本信息
本次完成所有调查人群共5 092人，最终纳入4 587人进行分析，见图1。本次分析人群年龄（56.3±9.60）岁，COPD患者年龄（62.32±9.36）岁，非COPD患者（55.76±9.42）岁。男性2 479人（54.04%）、女性2 108人（45.96%），男性患者占比为72.32%，多于女性患者的27.68%。
不同年龄、不同性别、城乡、BMI等人口学特征，14岁前是否经常咳嗽、15～17岁因肺炎或支气管炎住院、患哮喘、患支气管扩张症、患高血压等个人疾病史，父母患哮喘、父母患支气管扩张症等家族疾病史，是否经常咳嗽、是否起床咳嗽、是否晚上咳嗽、是否经常咳痰、是否起床咳痰、是否反复发作的喘息、是否气短或呼吸困难等个人相关症状，每日吸烟量增加、烹饪燃料与排风、有害气体暴露等个人相关危险因素暴露在COPD患者与非COPD患者中，差异具有统计学意义（P均<0.05），见表1。
2.2　COPD多因素logistic回归分析
将年龄、性别、城乡、BMI等21个变量纳入非条件多因素logistic回归分析。结果显示，年龄每增加1岁，COPD的患病风险增加5.3%；14岁前经常咳嗽的人群患COPD的风险是未咳嗽人群的2.41倍；哮喘人群患COPD的风险是未患哮喘人群的2.65倍；每日吸烟量处于0.1～19.9支的人群，其患COPD的风险是不吸烟的人群的1.96倍；相较于烹饪使用清洁燃料且排风的人群，烹饪使用清洁燃料但不排风人群患COPD的风险增加60.9%，烹饪使用污染燃料且不排风人群患COPD风险增加36.3%；相较于没有有害气体暴露史的人群，具有有害气体暴露史人群患COPD的风险增加35.8%。见表2。
2.3　COPD患病筛检模型效果与比较
本研究训练集COPD患者和非COPD患者人数分别为323和3 346例，测试集人数分别为78和840例。通过ROSE包ovun.sample函数对训练集进行平衡处理，将非COPD患者数量减少，患者数量增加，平衡后训练集的COPD患者和非COPD患者分别为1 782和1 887例。以是否患COPD作为因变量，基于多因素logistic回归结果，年龄、14岁前经常咳嗽、哮喘、每日吸烟量（支）、有害气体暴露、烹饪燃料与排风6个因素作为筛检变量纳入模型。
训练集结果显示：RF模型的AUC最大，为87.14%，其次是LR模型，AUC为73.64%，经delong法检验，两者差异有统计学意义（Z=26.954，P<0.001）；最后为SVM模型，AUC为73.30%，与RF模型相比，差异具有统计学意义（Z=28.091，P<0.001），与LR模型相比，差异无统计学意义（Z=1.814， P= 0.070）。表明LR模型和SVM模型在AUC性能方面表现相当，RF模型性能最好。见表3、图2。
测试集结果显示：LR模型的AUC最大，为76.10%，其次是SVM模型，AUC为76.08%，经delong法检验，两者差异无统计学意义（Z=0.026，P=0.980）；最后为RF模型，AUC为70.96%，与LR模型相比，差异有统计学意义（Z=-2.925，P=0.003），与SVM模型相比，差异有统计学意义（Z=-3.078， P=0.002）。表明LR模型和SVM模型在AUC性能方面表现相当，RF模型性能最差。见表3、图3。
3　讨论
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Wang等[8]研究显示我国成年人中COPD患者接近1亿，但仅12%的COPD患者做过肺功能检测，多数患者并不知晓自己患病。目前COPD确诊主要依靠肺功能检测，而作为金标准的肺功能检测成本高、操作难，大规模应用于人群筛查具有一定局限性。机器学习算法已被广泛应用于疾病筛检，国内研究使用LR、SVM、RF、决策树、神经网络等多种方法建立高血压、糖尿病及动脉粥样硬化等[9-14]筛检模型。多个地区均在探索COPD早筛工具，周家为等[15]使用多种问卷模型筛检COPD风险人群，其中《慢阻肺人群筛查问卷》效果最佳，该问卷包括年龄、吸烟、呼吸、气短及咳痰5个变量，其特异度、约登指数均低于本研究，分别为58.25%、37.00%。李章龙[16]通过BMI、家族呼吸系统疾病史及生物燃料暴露等16种变量构建SVM、决策树等五种筛检模型，其中RF效果最佳，AUC=96.35%。Wang等人[4]通过烹饪燃料类型、烹饪排风及支气管扩张实验后FEV1等指标列线图来筛检CODP患者，模型AUC=81%。虽然上述研究筛检效果较好，但其纳入变量较多、且包含体格检查与肺功能检查指标，收集较麻烦，不便于基层医疗机构使用。本研究建立的三种筛检模型仅纳入年龄及哮喘等6个简单变量，相较其他地区的多个筛检变量、体检指标或肺功能检测指标[4,16-17]，变量相对简单、易于收集。且本研究结果显示测试集LR、RF、SVM的AUC分别为76.10%、70.96%、76.08%，均具有较好的筛检效果，相较上述提到的其他模型，更适宜在基层医疗机构进行推广。
本研究使用delong法比较三种模型的筛检效果，训练集结果显示LR模型和SVM模型在AUC性能方面表现相当，RF模型性能最好。测试集结果显示LR模型和SVM模型在AUC性能方面表现相当，RF模型性能最差。RF是集成学习方法，可同时纳入定性和定量变量，是一个包含许多随机生成的决策树的集成分类器[18]。李章龙[16]构建多种COPD筛检模型，发现RF效果最佳，但王娇娇[9]发现RF预测钢铁工人动脉粥样硬化风险的效果低于SVM。本研究训练集结果显示RF筛检效果强于SVM与LR，但在测试集中弱于LR与SVM，可能是RF模型在训练集上的学习能力过强，发生过拟合[9]，或是因为测试集数据有限，限制了RF抽样随机的优势。SVM是二分类的监督学习模型，可将高维大数据分类为少量的数据点，从而在短时间内实现类别的区分[19]，在疾病筛检方面得到广泛应用[20]。本次研究显示测试集SVM模型的AUC、约登指数分别为76.08%、42.60%，灵敏度、特异度分别为73.08%、69.52%，表明该模型筛检效果较好，准确判定调查对象COPD患者的筛检价值较高。LR是经典方法，适用于二分类结局资料，本次研究结果显示测试集LR模型的AUC（76.10%）、灵敏度（70.51%）与特异度（71.31%）均较高，显示该模型在调查对象中准确判断COPD患者的筛检价值较高。
综上所述，基于LR、RF、SVM构建的COPD患者筛检模型，测试集中SVM模型与LR模型性能相当，可为COPD筛检提供参考。此外，构建模型所纳入的6个变量都较易获得，由此构建的筛检工具使用方便、操作简单，基层医疗机构可用其筛检COPD患者，提醒筛查患者进行确诊与治疗，做到COPD的“三早预防”，提升患者的生活质量。由于本研究仅进行内部验证，缺乏外部验证，所以存在一定局限性；且本研究基于横断面研究数据，下一步需要进行前瞻性研究来验证该模型的可靠性。
基金
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  • 基于医防融合的健康管理中心大数据平台研究与示范(黔科合支撑［2021］一般447)
  • 贵州省2019年中央补助地方重大疾病防治项目
  • 贵州省卫生健康委省级重点建设学科项目
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2024年第51卷第9期
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doi: 10.20043/j.cnki.MPM.202312018
  • 接收时间：2023-12-01
  • 首发时间：2026-03-18
  • 出版时间：2024-05-10
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  • 收稿日期：2023-12-01
基金
基于医防融合的健康管理中心大数据平台研究与示范(黔科合支撑［2021］一般447)
贵州省2019年中央补助地方重大疾病防治项目
贵州省卫生健康委省级重点建设学科项目
作者信息
    1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113
    2.贵州省疾病预防控制中心，贵州 贵阳 550004

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刘涛，E-mail:
参考文献
分享链接
https://castjournals.cast.org.cn/joweb/xdyfyx/CN/10.20043/j.cnki.MPM.202312018
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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