A New Hybrid Machine Learning Approach for Prediction of Phenanthrene Toxicity on Mice

Abstract

Phenanthrene, a PAH with three fused benzene rings, is usually used as a model for the study on PAHs. During 4 days, 166 male mice were equally and randomly divided into two groups. One group was given vehicle-corn oil by oral gavage, the other was given phenanthrene at a dose of 450 milligrams per kilogram per day. In this study, in order to predict mice's phenanthrene poisoning by virtue of blood analysis indices, a new machine learning approach was put forward, which was based on an improved binary mothflame optimizer combined with extreme learning machine. The results of the experiment have manifested that the blood analysis indices of the control and phenanthrene groups were significantly different (p < 0.5). The most important correlated indices including serum alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), plateletcrit (PCT) and red blood cell distribution width-standard deviation (RDW-SD) were screened through feature selection. The classification results demonstrated that the proposed method can achieve 93.38% accuracy and 98.33% specificity. Promisingly, there is a new and accurate way to detect the status of phenanthrene poisoning expectably. INDEX TERMS Phenanthrene, hepatotoxicity, moth-flame optimization algorithm, feature selection, extreme learning machine.

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