Research on Noninvasive Risk Evaluation of Diabetes Mellitus Based on Neural Network Pattern Recognition

Advanced glycation end products (AGEs) are highly associated with hyperglycemia in human skin tissue, and they also have the autofluorescence characteristic. A self-developed optical noninvasive detection device was used to measure the autofluorescence in human skin tissue, and then a neural network pattern recognition model was used to assess the risk of diabetes mellitus of the subject under survey. After the fluorescence spectra were acquired and processed with principal component analysis, four of the leading principal components were chosen to represent a whole spectrum. The established neural network pattern recognition model has 4 input nodes, 6 hidden nodes and 1 output node. A dataset consisting of 487 cases collected in Anhui Provincial Hospital was used to train the model. Seventy percent cases were used as the training set, 15% as the validation set and 15% as the test set. The model can output subjects risk of diabetes mellitus, or a dichotomous judgment. Receiver operating characteristic curve can be drawn with the area under curve of 0. 81, with standard error of 0. 02. When using 0. 5 as the threshold between diabetes mellitus and non-diabetes mellitus, the sensitivity and specificity of this model is 72. 4% and 77. 6% respectively, and the overall accuracy is 74. 9%. The method using human skin autofluorescence spectrum combined with neural network pattern recognition model is proposed for the first time, and the results show that this method has a better screening effect compared with currently used fasting plasma glucose and HbA1c.