Diagnosis Model of Paraquat Poisoning Based on Machine Learning

(E-pub Ahead of Print)

Author(s): Xianchuan Wang, Hongzhe Wang, Shuaishuai Yu, Xianqin Wang*

Journal Name: Current Pharmaceutical Analysis

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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.

Keywords: Machine learning, SVM, metabolomics, gas chromatography-mass spectrometry, paraquat, poisoning.

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(E-pub Ahead of Print)
DOI: 10.2174/1573412917666210302150150

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