Random forests for automatic differential diagnosis of erythemato-squamous diseases

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@Article{El-Bachir-Menai:2015:IJMEI,

• author = "Mohamed {El Bachir Menai}",
• title = "Random forests for automatic differential diagnosis of erythemato-squamous diseases",
• journal = "International Journal of Medical Engineering and Informatics",
• publisher = "Inderscience Publishers",
• year = "2015",
• month = apr # "~04",
• volume = "7",
• number = "2",
• pages = "124--141",
• ISSN = "1755-0661",
• keywords = "genetic algorithms, genetic programming, erythemato-squamous diseases, ESD, automatic differential diagnosis, decision trees, random forests, boosting, skin diseases, dermatology, classifiers",
• bibsource = "OAI-PMH server at www.inderscience.com",
• URL = "http://www.inderscience.com/link.php?id=68506",
• DOI = "doi:10.1504/IJMEI.2015.068506",
• 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 98percent, was also better than those of classifiers based on genetic programming, genetic algorithms and k-means clustering.",

}

Genetic Programming entries for Mohamed El Bachir Menai

Citations