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.
Graphical Abstract
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