基于神经网络的医疗诊断研究

Medical Diagnosis Based on Neural Network

主要工作集中在医疗数据的预处理和神经网络模型的训练.首先结合数据挖掘和自然语言处理技术,在保留医疗数据语义基础上对分词工具语料库进行补充,将中文数据分词,同时对其中大量冗余信息进行清洗,将中文数据转换成计算机可识别的编码,其次利用多种经典热门的神经网络模型来训练医疗数据,同时对比基于传统决策树模型的GBDT模型的训练结果,最后实验结果证明,对于多种疾病诊断,神经网络模型的效果要优于其他模型,诊断准确率接近90%.

In the report,the main work was focused on the preprocessing of medical data and the training of neural network model.Firstly,combined with data mining and natural language processing technology,the corpus of word segmentation tool was supplemented on the basis of preserving the semantics of medical data,and Chinese data is segmented.At the same time,a large number of redundant information was cleaned,and Chinese data was converted into computer recognizable coding.Secondly,many classical popular neural networks were used,and the network model was used to train medical data,and the training results of GBDT model based on traditional decision tree model were compared.Finally,the experimental results showed that the effects of neural network model was better than that of the other models for diagnosis of various diseases,and the diagnostic accuracy was close to 90%.