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

• Published in: Data & knowledge engineering Vol. 106; pp. 1 - 17
• Main Authors: Shrivastava, Vimal K., Londhe, Narendra D., Sonawane, Rajendra S., Suri, Jasjit S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-11-2016
• Subjects: Classification; Dermatology; Feature space; PCA; Reliability; Stability; Feature space; Stability; Reliability; Dermatology; Classification; PCA
• ISSN: 0169-023X; 1872-6933
• DOI: 10.1016/j.datak.2016.09.001

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.
•Psoriasis decision support system design to model the automated classification•Novel feature space extraction for psoriasis•PCA-based feature selection for psoriasis decision support design•Feature retaining power at different PCA cutoffs for optimal feature selection•Design of novel reliability and stability of psoriasis decision support system