Title: Random forests for automatic differential diagnosis of erythemato-squamous diseases

Authors: Mohamed El Bachir Menai

Addresses: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia

Abstract: Erythemato-squamous diseases (ESD) are frequent skin diseases that share some clinical features of erythema and scaling. Their automatic diagnosis was tackled using several approaches that achieved high performance accuracy. However, they generally remained unattractive for dermatologists because of the lack of direct readability of their output models. Decision trees are easy to understand, but their performance and structure are very sensitive to data changes. Ensembles of decision trees were introduced to reduce the effect of these problems, but on the expense of interpretability. In this paper, we present the results of our investigation of random forests and boosting as ensemble methods for the differential diagnosis of ESD. Experiments on clinical and histopathological data showed that the random forest outperformed the other ensemble classifiers in terms of accuracy, sensitivity and specificity. Its diagnosis accuracy, attaining more than 98%, was also better than those of classifiers based on genetic programming, genetic algorithms and k-means clustering.

Keywords: erythemato-squamous diseases; ESD; automatic differential diagnosis; decision trees; random forests; boosting; skin diseases; dermatology; classifiers.

DOI: 10.1504/IJMEI.2015.068506

International Journal of Medical Engineering and Informatics, 2015 Vol.7 No.2, pp.124 - 141

Received: 30 Dec 2013
Accepted: 08 Jul 2014
Published online: 04 Apr 2015 *

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