[1]常 伟),丁明翠),焦 洁),等.基于3种细胞因子的尘肺病筛查支持向量机模型的建立[J].郑州大学学报(医学版),2019,(06):811-814.[doi:10.13705/j.issn.1671-6825.2019.04.091]
 CHANG Wei),DING Mingcui),JIAO Jie),et al.Establishment of support vector machine model for pneumoconiosis screening based on three cytokines[J].JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES),2019,(06):811-814.[doi:10.13705/j.issn.1671-6825.2019.04.091]
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基于3种细胞因子的尘肺病筛查支持向量机模型的建立()
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《郑州大学学报(医学版)》[ISSN:1671-6825/CN:41-1340/R]

卷:
期数:
2019年06期
页码:
811-814
栏目:
论著
出版日期:
2019-11-20

文章信息/Info

Title:
Establishment of support vector machine model for pneumoconiosis screening based on three cytokines
作者:
常 伟1)丁明翠2)焦 洁3)王 威2)姚 武2)
1)平煤神马医疗集团总医院疾控中心 河南平顶山 467000 2)郑州大学公共卫生学院劳动卫生与职业病学教研室 郑州 450001 3)河南省职业病防治研究院 郑州 450052
Author(s):
CHANG Wei1) DING Mingcui2) JIAO Jie3) WANG Wei2) YAO Wu2)
1)Center for Disease Control, General Hospital of Pingmei Shenma Medical Group, Pingdingshan,Henan 467000 2)Department of Occupational Health and Occupational Disease, College of Public Health, Zhengzhou University, Zhengzhou 450001 3)Henan Institute of Occupational Health, Zhengzhou 450052
关键词:
尘肺病 支持向量机模型 Fisher判别分析模型 转化生长因子β1 血小板源性生长因子 结缔组织生长因子 筛查
Keywords:
pneumoconiosis support vector machine model Fisher discrimination analysis model transforming growth factor-β1 platelet derived growth factor connective tissue growth factor screening
分类号:
R563
DOI:
10.13705/j.issn.1671-6825.2019.04.091
摘要:
目的:建立基于转化生长因子β1(TGF-β1)、血小板源性生长因子(PDGF)、结缔组织生长因子(CTGF)的支持向量机模型(SVM)用于尘肺病的筛查。方法:选择70例男性尘肺病患者(尘肺病组),77例体检健康的男性(对照组),分别采集外周血并分离血清。采用ELISA法检测血清中TGF-β1、CTGF、PDGF的含量。采用SPSS Clementine软件分别构建Fisher判别分析模型和SVM模型,比较2种模型诊断尘肺病的效能。结果:基于血清TGF-β1、PDGF、CTGF含量建立的Fisher判别分析模型诊断尘肺病的准确度、灵敏度、特异度分别为78.1%、95.0%、61.9%,而SVM模型的准确度、灵敏度、特异度分别为87.8%、95.0%、81.0%; SVM模型的AUC为0.908,优于Fisher判别分析模型(0.830)(Z=3.181,P=0.002)。结论:建立了基于人血清TGF-β1、PDGF、CTGF含量、可用于尘肺病筛查的SVM模型,且筛查效果较好。
Abstract:
Aim:To establish a support vector machine(SVM)model for screening pneumoconiosis based on transforming growth factor-β1(TGF-β1), platelet derived growth factor(PDGF), and connective tissue growth factor(CTGF).Methods:Seventy males with pneumoconiosis(pneumoconiosis group)and 77 healthy males(control group)were selected.Peripheral blood of the subjects was collected, and the serum was separated. The contents of TGF-β1, CTGF,and PDGF were determined by ELISA. SPSS Clementine software was used to construct Fisher discrimination analysis model and SVM model. The efficacy of 2 models for screening pneumoconiosis was compared.Results:The accuracy, sensitivity, and specificity of Fisher discrimination analysis model in diagnosis of pneumoconiosis were 78.1%, 95.0%, 61.9%, and those for SVM model were 87.8%, 95.0%, 81.0%, respectively. The AUC of SVM model was 0.908, higher than that(0.830)of Fisher discrimination analysis model(Z=3.181,P=0.002).Conclusion:SVM model based on serum TGF-β1, CTGF,and PDGF has been established with better prediction efficacy in pneumoconiosis screening.

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备注/Memo

备注/Memo:
【基金项目】国家自然科学基金面上项目(81773404); 平煤神马医疗集团总医院2018年科技计划项目 【作者简介】姚武,通信作者,男,1962年8月生,博士,教授,研究方向:职业卫生,E-mail:yaowu@zzu.edu.cn
更新日期/Last Update: 2019-11-20