Multiclass support vector machines for diagnosis of erythemato-squamous diseases

Abstract

A new approach based on the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for diagnosis of erythemato-squamous diseases. The recurrent neural network (RNN) and multilayer perceptron neural network (MLPNN) were also tested and benchmarked for their performance on the diagnosis of the erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the classifiers learned how to differentiate a new case in the domain. The classifiers were used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the multiclass SVM and RNN trained on these features achieved high classification accuracies.