Skin diseases such as melanoma and carcinoma are often very difficult to detect at an early stage and are even more difficult to differentiate. Recently, it is well known that melanoma is the most dangerous form of skin cancer among other types of skin cancer because it is more likely to spread to other parts of the body if it is not diagnosed and treated early.To classify these skin diseases, a machine learning algorithm called "Support Vector Machine (SVM)" can be used.In this paper, we propose a method to identify Support Vector Machine (SVM), a traditional kernel-based method and a very good nonlinear classifier, is discussed to diagnose regular(common) skin disorders. SVM is originally designed for binary classification, but we have applied it for multiclass data (Dataset-I) using one-to-one algorithm (refer 2.4.1). Kernel functions in SVM plays a very important role in classification accuracy.

SVM, SMO, QP, MATLAB