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Differential Diagnosis of Erythematous Squamous Diseases With Feature Selection and Classification Algorithms
Abstract
In this chapter, the differential diagnosis of erythematous diseases was determined using data mining and machine learning algorithms. In this chapter, data mining and its application to differential diagnosis of erythematous squamous diseases were discussed. A dermatology dataset from UCI Machine Learning Repository was used for the study. The dataset consists of 366 data items with 34 attributes. Initially, feature selection was made, and then classification was performed by using various algorithms. The number of attributes has been reduced from 34 to 19 as a result of the integration of the correlation-based filter methods and various heuristic search methods. The evaluation results show that Naive Bayes has 100% success rate in classification of psoriasis, seborrheic dermatitis, lichen planus, rose disease, chronic dermatitis, and pityriasis rubra pilaris diseases with 19 attributes selected with feature extraction algorithms.
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