A novel approach to multiclass psoriasis disease risk stratification

Highlights • Multiclass risk assessment and stratification system for psoriasis disease. • Comparison of four kinds of systems: SVM-PCA, SVM-FDR, DT-PCA and DT-FDR. • Comprehensive feature space of 859 features. • Performance evaluation using feature retaining power and aggregate feature effect. • Classification accuracy of 99.92% and system reliability of 99.73%. Abstract The stage and grade of psoriasis severity is clinically relevant and important for dermatologists as it aids them lead to a reliable and an accurate decision making process for better therapy. This paper proposes a novel psoriasis risk assessment system (pRAS) for stratification of psoriasis severity from colored psoriasis skin images having Asian Indian ethnicity. Machine learning paradigm is adapted for risk stratification of psoriasis disease grades utilizing offline training and online testing images. We design four kinds…

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