Identification of Ketamine-addicted Animal Models Based on Radial Basis Function Network

Ketamine is a general anesthetic given intravenously in clinic, mainly used for surgical anesthesia, anesthesia induction and clinical anesthesia for diagnostic tests. Ketamine is also abused as a recreational drug because of its analgesic effect, hallucinogenic effect and addictive quality. It has emerged as an increasingly popular drug among young drug abusers worldwide, and its consumption is on the rise. A total of 29 rats were used in our study, ketamine abuse group (n = 15) and control group (n = 14). Our study to detect the changes in brain tissue of rats in two group by gas chromatography-mass spectrometry (GC-MS) and got the data sets. Then, we explore to use a machine learning method, the radial basis function network (RBFN), for the identification of animal ketamine addiction. This approach achieved good results with precision of 93.1034%. Experiments show that RBFN can achieve better results or very competitive results than other involved counterparts in precision and training time. In addition, the Relief algorithm was used to select the markers and analysis the change of the markers, which can be used to provide references for the clinical rational drug use and new ideas for the identification of drug dependence.