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 data set, and then uses the SVM classification algorithm in machine learning to train the
hydrogen sulfide poisoning training data set 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 gain attribute evaluation method to screen out the top
6 biomarkers that contribute to the predicted category (Glycerol,β-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.