Title:Diagnosis Model of Paraquat Poisoning Based on Machine Learning
Volume: 18
Issue: 2
Author(s): Xianchuan Wang, Hongzhe Wang, Shuaishuai Yu and Xianqin Wang*
Affiliation:
- Analytical and Testing Center, School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035,China
Keywords:
Machine learning, SVM, metabolomics, gas chromatography-mass spectrometry, paraquat, poisoning.
Abstract:
Background: The objective of this research was to screen metabolites with specificity
differences in the lung tissue of paraquat-poisoned rats by metabolomics technology and chi-square
test method, to provide a theoretical basis for the study of the mechanisms of paraquat poisoning,
and to use machine learning technology to construct a paraquat poisoning diagnosis model. This
provided an intelligent decision-making method for the diagnosis of paraquat poisoning.
Methods: 18 paraquat-poisoned rats (36 mg/kg) and 16 positive control rats were selected. Lung tissue
from each rat from both groups was extracted and analyzed by GC-MS. The chi-square test for
feature evaluation was used to screen the difference in specific metabolites in the lung tissue between
the paraquat-poisoned rats and the control group, and the SVM classification machine learning
algorithm was used to construct an intelligent diagnosis model.
Results: In the end, a total of 14 significant metabolic differences were identified between the two
groups (P < 0.05). The sensitivity, specificity, and accuracy of the constructed SVM paraquat poisoning
diagnostic model reached 95%, 95% and 96.67%, respectively.
Conclusion: Based on metabolomics technology, the chi-square test for feature evaluation was
used to successfully screen the changes of specific metabolites produced in the lungs after paraquat-
poisoning, and the diagnosis model based on SVM was constructed to provide an intelligent decision
for the diagnosis of paraquat poisoning.
