A New Evolutionary Machine Learning Approach for Identifying Pyrene Induced Hepatotoxicity and Renal Dysfunction in Rats

Pyrene, composed of four fused benzene rings, is a polycyclic aromatic hydrocarbon (PAH) that has served as a model compound for evaluating the toxic effects of PAHs. In this paper, 114 male rats were dosed daily by oral gavage with either vehicle (corn oil) or pyrene (1500 mg/kg/day) for four days. A method based on the gray wolf optimization-enhanced machine learning approach was then developed to identify pyrene poisoning in rats using the indices from blood analysis. The results showed that there were significant differences in blood analysis indices between the control and the pyrene groups (p <; 0.05$ ). In terms of feature selection, the most important correlated indices were liver to body weight ratio, the absolute value of leukomonocyte, the percentage of monocyte, serum albumin, direct bilirubin, urea nitrogen, and uric acid. This method was shown to have an accuracy rate of 94.62% (ACC), 0.8988 Matthews correlation coefficients (MCCs), 91.71% sensitivity, and 98.33% specificity. Empirical analysis indicates that this method represents a new and accurate approach for detecting pyrene poisoning.