Comparison of LVQ and BP Neural Network in the Diagnosis of Diabetes and Retinopathy

Abstract

Diabetes Mellitus is a chronic, non-infectious disease that affects people’s health, which has a rapid onset trend with the continuous improvement of China’s economy and material life. At present, there is no practical solution to this condition. Diabetic retinopathy (DR) is the most important manifestation of diabetic microangiopathy, which can be divided into proliferative lesion and non-proliferative lesion. The traditional artificial diagnosis method has strong subjectivity and low accuracy. Because disease diagnosis can be regarded as a two-classification pattern recognition problem, the application of neural network method can provide the possibility for the application of artificial intelligence (AI) in disease-assisted diagnosis and treatment. Furthermore, it has significant meanings to find which kind of neural network has a better efficiency. In this paper, learning vector quantization (LVQ) neural network and back propagation (BP) neural network were used to diagnose diabetes and diabetic retinopathy with MATLAB and their recognition rate were compared. The datasets of diabetes and diabetic retinopathy were available in the UCI database. The experiment results were analyzed to evaluate the efficiency of each neural network classifier. The results demonstrate that both LVQ neural network and BP neural network can classify the two datasets effectively. However, compared with the LVQ neural network, the average accuracy rate and sensitivity of the BP neural network is higher.