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Ying Y, Jin Y, Wang X, Ma J, Zeng M, Wang X. Diagnosis Model of Hydrogen Sulfide Poisoning Based on Support Vector Machine. CURR PHARM ANAL 2021. [DOI: 10.2174/1573412916999200727181005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Introduction:
Hydrogen sulfide (H2S) is a lethal environmental and industrial poison. The mortality rate of
occupational acute H2S poisoning reported in China is 23.1% ~ 50%. Due to the huge amount of information on
metabolomics changes after body poisoning, it is important to use intelligent algorithms to mine multivariate interactions.
Methods:
This paper first uses GC-MS metabolomics to detect changes in the urine components of the
poisoned group and control rats to form a metabolic dataset, and then uses the SVM classification algorithm
in machine learning to train the hydrogen sulfide poisoning training dataset to obtain a classification
recognition model. A batch of rats (n = 15) was randomly selected and exposed to 20 ppm H2S
gas for 40 days (twice morning and evening, 1 hour each exposure) to prepare a chronic H2S rat poisoning
model. The other rats (n = 15) were exposed to the same volume of air and 0 ppm hydrogen
sulfide gas as the control group. The treated urine samples were tested using a GC-MS.
Results:
The method locates the optimal parameters of SVM, which improves the accuracy of SVM
classification to 100%. This paper uses the information to gain an attribute evaluation method to screen
out the top 6 biomarkers that contribute to the predicted category (Glycerol, -Hydroxybutyric acid,
arabinofuranose, Pentitol, L-Tyrosine, L-Proline).
Conclusion:
The SVM diagnostic model of hydrogen sulfide poisoning constructed in this work has training time and
prediction accuracy; it has achieved excellent results and provided an intelligent decision-making method for the diagnosis
of hydrogen sulfide poisoning.
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Affiliation(s)
- Yifan Ying
- Information Technology Center, Wenzhou Medical University, Wenzhou,China
| | - Yongxi Jin
- Department of Rehabilitation, Wenzhou Municipal Hospital of Traditional Chinese Medicine, Wenzhou,China
| | - Xianchuan Wang
- Information Technology Center, Wenzhou Medical University, Wenzhou,China
| | - Jianshe Ma
- School of Basic Medicine, Wenzhou Medical University, Wenzhou,China
| | - Min Zeng
- Network Information Center, Wenzhou Vocational College of Science and Technology, Wenzhou,China
| | - Xianqin Wang
- Analytical and Testing Center of Wenzhou Medical University, Wenzhou,China
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