Reliability analysis of psoriasis decision support system in principal component analysis framework

Abstract Reliability and accuracy are essential components in any decision support system. These become even more important with a rising number of features during the classification process in a machine learning paradigm. Further, the selection of an optimal feature set is of paramount importance for the best performance, reliable and stable decision support systems. This paper presents a dermatology decision support system used for the classification of psoriasis images into diseased and healthy skin. A comprehensive grayscale and color feature space with 87 features are explored. The classification system consists of a machine learning paradigm embedded with principal component analysis-based optimal feature selection. The system consists of both offline training classifier and online testing classifier phases. The training parameters are estimated using unique feature space and ground truth, a priori derived by the dermatologist. The training phase generates the offline coefficients using a training classifier which is then used for transforming the online test features for prediction of two skin classes: diseased vs. healthy. The proposed system using principal component analysis shows the best classification accuracy of 99.39% for a 10-fold cross-validation using polynomial kernel of order-2 on a set of 540 images. We validate our system by computing the reliability and stability indices. The results demonstrate a mean reliability index of 98.71% for 11 distinct data sizes, and meeting the stability criteria within 2% tolerance. The ability to retain the dominant features by inclusion of increasing set of features is 90.52%. Thus proposed system shows the encouraging results with higher accuracy, reliability, stability and retaining power of dominant features.