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Current Pharmaceutical Analysis

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ISSN (Print): 1573-4129
ISSN (Online): 1875-676X

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Research Article

Diagnosis Model of Hydrogen Sulfide Poisoning Based on Support Vector Machine

Author(s): Yifan Ying, Yongxi Jin, Xianchuan Wang, Jianshe Ma, Min Zeng* and Xianqin Wang*

Volume 17, Issue 8, 2021

Published on: 27 July, 2020

Page: [1036 - 1042] Pages: 7

DOI: 10.2174/1573412916999200727181005

Price: $65

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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 investigate 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, &946;-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.

Keywords: Hydrogen sulfide, metabolomics, poisoning, SVM, GC-MS, machine learning.

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