基于SMOTE和RNN的肾移植排斥反应预测

肾移植手术在当今的应用越来越广泛，对于排斥反应的预测变得更加重要。针对排斥反应数据特点中存在的数据的维度高、数据时序性、样本不均衡等问题，将循环神经网络应用于肾移植排斥反应的预测,本文提出一种结合SMOTE(Synthetic Minority Over-sampling Technique)以及RNN(Recurrent Neural Network)的算法。该方法先处理数据，降低正负样本的不平衡度，且解决样本量不足的问题，再根据RNN的学习过程进行关键参数调整、优化。经过实验发现，该方法可以有效提升正负分类的准确率，与传统的马尔可夫时间序列预测算法相比，准确率提高了16.7%，传统RNN训练经过优化后，相对错误率下降了5.03%，可以使用该方法进行肾移植排斥反应的有效预测。

Prediction of Renal Transplantation Rejection Based on SMOTE and RNN

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.