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Evaluation of the Feature Space of an Erythematosquamous Dataset Using Rough Sets
"... 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 d ..."
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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,
Research Article A Survey on Utilization of the Machine Learning Algorithms for the Prediction of Erythemato Squamous Diseases
, 2014
"... Abstract: The aim of this study list the contributions of various machine learning algorithms for the prediction of Erythemato Squamous Diseases (ESDs) and it is very useful for the budding researchers to do research in this field. In the advent of ozone depletion the ultra violet radiation is the ..."
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Abstract: The aim of this study list the contributions of various machine learning algorithms for the prediction of Erythemato Squamous Diseases (ESDs) and it is very useful for the budding researchers to do research in this field. In the advent of ozone depletion the ultra violet radiation is the major cause of many skin diseases, which are leading to skin cancer. Early detection of skin cancer is more important to avoid human loses and especially the white skinned people are more affected. The Asian and African race people are less affected as they have melanin in their skin. The American's are directly and more widely affected by the ozone depletion, due to this ESD, which is predominant among the skin diseases. Due to technology advancements a large amount of data are deposited. In these data the information is hidden as raw data and with latest methodologies and technologies like Data Mining, neural networks, fuzzy systems, Genetic and Evolutionary computing a pattern can be evolved to study them.
