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Glyphosate, one of the most popular herbicides world-wide, is used as an active ingredient in many commercial formulations. So far, many studies have focused on reproductive toxicity on glyphosate which may trigger epigenetic changes through endocrine-mediated mechanisms. Generally, it is critical to identify the glyphosate poisoning status in order to minimize health risks. This paper proposed a machine learning approach using 110 rats' samples to identify the glyphosate poisoning status. All rats were randomly divided into 2 groups (n = 55 each) in the study. The rats in the blank control group were given 0.9% sodium chloride solution orally for 15 days, while the other groups were administered orally at the dosage of 0.5g/kg of glyphosate once per day for 15 days consecutively. Consequently, the potential of an enhanced fuzzy k nearest neighbor approach was explored through blood routine test to recognize the glyphosate poisoning condition based on the blood indices. A real-life data set was used to evaluate the effectiveness of the proposed method in terms of classification accuracy (ACC), sensitivity, specificity, and Matthews Correlation Coefficients (MCC). The results demonstrated that there were significant differences in blood routine test indices between the control and the glyphosate groups (p-value <; 0.01). It was observed in the feature selection that the most important correlated indices were white blood cell , lymphocyte , intermediate cell, and neutrophil granulocyte. A comparative study with support vector machine and artificial neural networks had shown that the proposed approach could achieve much more promising results with 95.45% ACC, 0.90 MCC, 98.89% sensitivity, and 88.89% specificity. The empirical analysis verifies that the proposed method is promising to act as a new, accurate method for identification of the glyphosate poisoning status in subjects.

An Effective Machine Learning Approach for Identifying the Glyphosate Poisoning Status in Rats Using Blood Routine Test