Automated identification of diabetes type-2 subjects with and without neuropathy using eigenvalues

Abstract Diabetes is a disorder of metabolism and has been a leading healthcare burden throughout the world. The most typical form of diabetes is type-2 diabetes. It is commonly developed in adults of age 40 and older. The purpose of this study is to identify the plantar pressure distribution in normal subjects, diabetic type-2 subjects with neuropathy, and diabetic type-2 subjects without neuropathy. Foot scan images were obtained using the F-Scan (Tekscan USA) in-shoe measurement system. The eigenvalues were evaluated from principal-component analysis after performing continuous wavelets transformation (CWT). The eigenvalues of CWT in regions 5 and 7 had shown excellent p values of more than 95 per cent confidence level when subjected to an analysis-of-variance test. These parameters were then presented to an artificial neural network (ANN) and a Gaussian mixture model (GMM) for automatic classification. The results show that the ANN classifier performs better than the GMM and is able to identify the unknown class with a sensitivity of 100 per cent and a specificity of 72 per cent.