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. Guvenir et al. (1998) studied about ESDs and contributed 366 patients data with 34 features consisting of clinical and histopathological data in the dermatology dataset (The data taken from School of Medicine in Gazi University and the department of Computer Science in Bilkent University, Turkey; and it is available in the URL (http://archive.ics.uci.edu/ml/datasets/Dermatology) in the year 1998. This survey study gives a brief description about the contribution of what in the field of ESDs in Chronological order from the year 1998 till 2013. In this study we intend to contribute various machine learning algorithms dealing with ESDs.

Keywords:

AdaBoost, Adaptive Neuro-Fuzzy Interference Systems (ANFIS), ESD, Extreme Learning Machine (ELM), fuzzy based ELM, fuzzy logic, preprocessing techniques, Support Vector Machine (SVM),

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