Yifan Ying, Yongxi Jin, Xianchuan Wang, Jianshe Ma, Min Zeng* and Xianqin Wang* Pages 1036 - 1042 ( 7 )
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.
Hydrogen sulfide, metabolomics, poisoning, SVM, GC-MS, machine learning.
Information Technology Center, Wenzhou Medical University, Wenzhou, Department of Rehabilitation, Wenzhou Municipal Hospital of Traditional Chinese Medicine, Wenzhou, Information Technology Center, Wenzhou Medical University, Wenzhou, School of Basic Medicine, Wenzhou Medical University, Wenzhou, Network Information Center, Wenzhou Vocational College of Science and Technology, Wenzhou, Analytical and Testing Center of Wenzhou Medical University, Wenzhou