基于增强稀疏自编码器与Softmax回归的医学诊断  

Medical diagnosis based on enhanced sparse autocoder and Softmax regression

作　　者：孟祥莲 蒋巍[1] 李晓芳[1] Meng Xianglian;Jiang Wei;Li Xiaofang(School of Computer Science and Information Engineering,Changzhou Institute of Technology,Changzhou 213032,China)

机构地区：[1]常州工学院计算机信息工程学院,常州213032

出　　处：《电子测量技术》2021年第11期114-119,共6页Electronic Measurement Technology

基　　金：国家自然科学基金(61901063);教育部人文社会科学研究青年基金(19YJCZH120);江苏高校“青蓝工程”基金(2020);常州市科技计划基金(CE20205042)项目资助。

摘　　要：为了提升医学诊断的预测精度,设计了增强稀疏自编码器和Softmax回归的特征学习和分类阶段组合方法。在稀疏自编码器(SAE)网络的特征学习中,通过惩罚网络的权重实现稀疏性,结合反向传播学习方法将变化向后传递并迭代优化成本函数。在Softmax回归分类阶段中,利用带动量的小批量梯度下降法来优化Softmax分类器的交叉熵,结合小批数据计算模型误差更新模型参数并实现收敛性。将所提出方法用于心脏病、宫颈癌和慢性肾病(CKD)数据集实验,其预测精度分别为91%、97%和98%,并且表现出较高的特征学习和鲁棒的分类性能。In order to improve the prediction accuracy of medical diagnosis,a feature learning and classification stage combination method based on enhanced sparse self encoder and Softmax regression is designed.In the feature learning of the sparse self encoder(SAE)network,the sparsity is realized by punishing the weight of the network,and the change is transmitted backward and the cost function is optimized iteratively.In the stage of Softmax regression classification,the cross entropy of Softmax classifier is optimized by small batch gradient descent method,and the model error is calculated with small batch data,and the model parameters are updated to achieve convergence.The prediction accuracy of the proposed method is 91%,97%and 98%respectively for heart disease,cervical cancer and chronic kidney disease(CKD)data sets,and shows high feature learning and robust classification performance.

关 键 词：稀疏自动编码器 无监督学习 Softmax回归 医学诊断 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]