To identify and design comfortable footwear for patients with diabetic conditions (e.g., high blood pressure), an experimental design was proposed to collect plantar pressure data-set using an RSscan sensor system. The data-set acquired by the pressure sensor was reformed into images to allow for image analysis technologies to be applied. In this paper, image features were extracted including color of Hue-Saturation-Value (HSV), gray difference based features (mean, entropy and auto-correlation function), gray-level co-occurrence matrix based features (energy and correlation) and Histogram of Oriented Gradient (HOG). The features were normalized into a high dimensional vector applied to a Fuzzy Support Vector Machine (FSVM), and finally, the FSVM was trained and used for prediction of diabetic plantar pressure images. Normal features and HOG were compared in different classifiers including SVM, LSVM and FSVM. HOG with normal features of image for FSVM performed with a higher accuracy classification effectiveness (84.3%) than the current state of the art. The proposed methods have clear applications in revealing the key zone of foot plantar of diabetics and offer a new direction in producing comfortable diabetic footwear.