Abstract

Info

Author(s):: CHEN Zhencheng1; YANG Xuebing1; ZOU Chunlin2; YAN Bowen1; ZHU Jianming3; LIANG Yongbo3; 1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China 2. Transforming Medical Research Center, Guangxi Medical University, Nanning 530021, China 3. School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China

Keywords:: Keywords: diabetes glycosylated hemoglobin impaired glucose tolerance neural network K-nearest neighbor algorithm

Abstract:: Abstract: On the basis of routine methods for diagnosing impaired glucose tolerance (IGT), an IGT detection model is constructed by adding glycosylated hemoglobin as the factor of diabetes screening, thereby improving the detection rate of pre-diabetes. The height, body weight, abdominal circumference, blood pressure, thickness of sebum, fasting blood glucose and glycosylated hemoglobin of subjects were collected as the feature inputs of the model, and then the subjects were classified by K-nearest neighbor algorithm and neural network. The output of the model included normal blood glucose, IGT and diabetes. The results show that after adding glycosylated hemoglobin as the factor of diabetes screening, the classification accuracies of neural network and K-nearest neighbor algorithm are 88.89% and 93.09%, respectively, which are significantly higher than 83.33% and 78.38% of traditional methods. The proposed IGT detection model is of great significance for the clinical diagnosis of diabetes.