Abstract

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Title:: Prediction model of diabetic peripheral neuropathy based on one-dimensional convolutional neural network

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

Keywords:: Keywords: diabetic peripheral neuropathy deep learning one-dimensional convolutional neural network data preprocessing

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.