The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases

Authors:
Kemal Polat;Salih Güneş
Affiliations:
Electrical and Electronics Engineering Department, Selcuk University, 42035 Konya, Turkey;Electrical and Electronics Engineering Department, Selcuk University, 42035 Konya, Turkey
Venue:
Digital Signal Processing
Year:
2006

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Abstract

This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first part, we have used decision tree classifier to diagnosis erythemato-squamous disease. In the second part, first of all, fuzzy weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified using decision tree classifier. In the third part, k-NN based weighted pre-processing, which can improved by ours, is a new method and applied to inputs erythemato-squamous disease dataset. Then, the obtained weighted inputs were classified via decision tree classifier. The employed decision tree classifier, fuzzy weighted pre-processing decision tree classifier, and k-NN based weighted pre-processing decision tree classifier have reached to 86.18, 97.57, and 99.00% classification accuracies using 20-fold cross validation, respectively.