[1]侯伟,赵耕,刘玉良,等.基于一维卷积神经网络的糖尿病周围神经病变预测模型研究[J].中国医学物理学杂志,2022,39(1):127-132.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.021]
　HOU Wei,ZHAO Geng,LIU Yuliang,et al.Prediction model of diabetic peripheral neuropathy based on one-dimensional convolutional neural network[J].Chinese Journal of Medical Physics,2022,39(1):127-132.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.021]
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基于一维卷积神经网络的糖尿病周围神经病变预测模型研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第1期
页码:
127-132
栏目:
医学人工智能
出版日期:
2022-01-17

文章信息/Info

Title:
Prediction model of diabetic peripheral neuropathy based on one-dimensional convolutional neural network
文章编号:
1005-202X（2022）01-0127-06
作者:
侯伟1; 赵耕2; 刘玉良1; 杨伟明1; 郭丽1
1.天津科技大学电子信息与自动化学院， 天津 300222； 2.天津医科大学代谢病医院检验科， 天津 300070
Author(s):
HOU Wei1;  ZHAO Geng2;  LIU Yuliang1;  YANG Weiming1;  GUO Li1
1. School of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China 2. Department of Laboratory, Metabolic Disease Hospital, Tianjin Medical University, Tianjin 300070, China
关键词:
糖尿病周围神经病变; 深度学习; 一维卷积神经网络; 数据预处理
Keywords:
Keywords: diabetic peripheral neuropathy deep learning one-dimensional convolutional neural network data preprocessing
分类号:
R318；R587.2
DOI:
DOI:10.3969/j.issn.1005-202X.2022.01.021
文献标志码:
A
摘要:
为了实现对糖尿病周围神经病变（DPN）的早期预防，辅助医生进行早期诊断与决策，提出了一种基于一维卷积神经网络的DPN预测模型，对原始数据进行了一系列的预处理工作以提高数据的质量，此外数据集的特征维度较高，为了进一步提高预测模型的准确性，进行了主成分分析（PCA）降维处理，通过自主学习数据的特征信息，从中挖掘其有价值的医学信息与规律，来实现DPN的预测。通过支持向量机、BP神经网络和一维卷积神经网络分别建立了DPN预测模型。实验结果表明，一维卷积神经网络模型预测效果优于其他两个模型，其准确率、召回率、F1值、AUC值分别达到了0.983、0.916、0.923、0.98。
Abstract:
Abstract: In order to achieve the early prevention of diabetic peripheral neuropathy (DPN), and assist doctors in early diagnosis and decision-making, a prediction model of DPN based on one-dimensional convolution neural network is proposed. A series of preprocessing is carried out on the original data to improve the quality of the data. In addition, due to the high feature dimensions of the data set, principal component analysis is used to reduce the dimensions, thereby further improving the accuracy of the prediction model. Through self-learning of the feature information of the data, the useful information such as medical information and laws is mined for achieving the prediction of DPN. The DPN prediction models are established by support vector machine, BP neural network and one-dimensional convolution neural network, separately. The experimental results show that the prediction effect of one-dimensional convolutional neural network model is better than that of the other two models, and its accuracy, recall rate, F1-score and AUC values are 0.983, 0.916, 0.923 and 0.98, respectively.

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备注/Memo

备注/Memo:
【收稿日期】2021-07-15 【基金项目】天津市科委技术创新引导专项（21YDTPJC00500）；教育部“天诚汇智”科研创新基金（2018A03033） 【作者简介】侯伟，硕士，研究方向：人工智能辅助诊断，E-mail: 2206736393@qq.com 【通信作者】刘玉良，副教授，研究方向：基于深度学习的智能医学辅助诊断、医用电子仪器产品开发，E-mail: ylliu@tust.edu.cn
更新日期/Last Update: 2022-01-17