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Establishment and verification of early screening model of chronic obstructive pulmonary disease based on three machine learning methods
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Ying-jiao MU1, Zi-yun WANG1, Xu SU2, Ling LI2, Jie ZHOU2, Yi-ying WANG2, Tao LIU1, 2
Modern Preventive Medicine | 2024, 51(9) : 1677 - 1683
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Modern Preventive Medicine | 2024, 51(9): 1677-1683
Health and Social Behavior
Establishment and verification of early screening model of chronic obstructive pulmonary disease based on three machine learning methods
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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
Published: 2024-05-10 doi: 10.20043/j.cnki.MPM.202312018
Outline
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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
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
Year 2024 volume 51 Issue 9
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Article Info
doi: 10.20043/j.cnki.MPM.202312018
  • Receive Date:2023-12-01
  • Online Date:2026-03-18
  • Published:2024-05-10
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History
  • Received:2023-12-01
Funding
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    Key Laboratory of Environmental Pollution and Disease Monitoring, Ministry of Education, School of Public Health and Health,Guizhou Medical University Guiyang, Guiyang 561113, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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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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