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.

背景：本研究的目的是通过代谢组学技术和卡方检验方法筛选百草枯中毒大鼠肺组织中具有特异性差异的代谢物，为百草枯中毒机制的研究提供理论依据，为百草枯中毒机制的研究提供理论依据。利用机器学习技术构建百草枯中毒诊断模型。这为百草枯中毒的诊断提供了一种智能决策方法。

方法：选择百草枯中毒大鼠18只（36 mg/kg），阳性对照大鼠16只。提取来自两组的每只大鼠的肺组织并通过 GC-MS 进行分析。采用特征评价的卡方检验筛选百草枯中毒大鼠与对照组肺组织中特定代谢物的差异，并采用SVM分类机器学习算法构建智能诊断模型。

结果：最终，两组间共鉴定出14处显着代谢差异（P < 0.05）。构建的SVM百草枯中毒诊断模型的敏感性、特异性和准确度分别达到95%、95%和96.67%。

结论：基于代谢组学技术，通过特征评价的卡方检验，成功筛选出百草枯中毒后肺部产生的特定代谢物的变化，构建基于SVM的诊断模型，为患者提供智能决策。百草枯中毒的诊断。