Abstract: Nowadays, kidney transplantation is more and more widely used, and the prediction of rejection becomes more and more important. In order to solve the problems of high dimension, time sequence and sample imbalance in rejection data, recurrent neural network is applied to predict renal transplantation rejection, in this paper, an algorithm combining SMOTE (Synthetic Minimum Over-sampling Technology) with RNN (Recurrent Neural Network) is proposed. The method first processes the data, reduces the imbalance between samples, and solves the problem of insufficient sample size. Then the key parameters are adjusted and optimized according to the learning process of RNN. The experimental results show that this method can effectively improve the accuracy of positive and negative classification. Compared with the traditional Markov time series prediction algorithm, the accuracy is improved by 16.7%. After the traditional RNN training is optimized, the relative error rate is reduced by 5.03%. This method can be used to effectively predict renal transplantation rejection.

Key words: renal transplantation rejection, sequence classification, RNN, SMOTE