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.
Highlights
The detection of ESDs is very difficult and it is a Herculean task as these diseases share common features like clinical and histopathological features with very minor differences
The results showed that the average predictive accuracy obtained by a “standalone” Support Vector Machine (SVM) or by a Random Subspace (RS) ensemble of SVM is less when compared to the proposed method
The classification accuracy of fuzzy extreme learning machine (FELM) based approach is reached to 94% before data preprocessing and the same is increased to 99.02% after data preprocessing
Summary
The detection of ESDs is very difficult and it is a Herculean task as these diseases share common features like clinical and histopathological features with very minor differences. Akin Ozcift, et al [18] proposed a new multi-class feature selection method based on rotation forest meta-learner algorithm. This proposed system eliminates the redundant attributes and the accuracy of this model is varying between 98% and 99%. Mohammad Javad Abdi, et al [23] developed a model which is based on Particle Swarm Optimization (PSO), SVMs and ARs. The proposed system achieves 98.91% classification accuracy using 24 features as their inputs. Badrinath et al [26] developed AdaBoost and Hybrid classifier methodologies along with Apriori and ARs data preprocessing In this case, the classification accuracy is increased to 99.26% and the computational is very high than FELM.
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