Evaluation of the Feature Space of an Erythematosquamous Dataset Using Rough Sets
Kenneth Revett, Florin Gorunescu, Abdel-Badeeh Mohamed Salem, El‐Sayed A. El‐Dahshan · 2009
Abstract. The differential diagnosis of erythematosquamous diseases remains a difficult task requiring both clinical and histopathological data to support a diagnosis. The principle reason for diagnostic ambiguity is based on the significant degree of overlap in the overt symptoms of this class of disease. Histopathological evidence can assist in making a positive diagnosis- but is labor and resource intensive. In order to evaluate the diagnostic veracity of clinical versus histopathological features of erythematosquamous diseases, a comparison of both fea-tures classes was evaluated using rough sets. The results indicate that the histopathological feature space provided a much more significant classification rate relative to clinical features. In addition, the results of this preliminary study indicate that only a small subset of the histopathological feature space is required for maximal classification accuracy. Key words and phrases. Datamining, Dermatology, Erythematosquamous Diseases, Reducts,