Differential diagnosis of Erythmato-Squamous Diseases using classification and regression tree

Introduction: Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the
field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process
for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different
algorithms were developed for this purpose. Objective: we aimed to use the Classification and
Regression Tree (CART) to predict differential diagnosis of ESD. Methods: we used the Cross Industry
Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set
from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company
was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and
specificity of the model. Results: The proposed model had an accuracy of 94.84% (Standard Deviation:
24.42) in order to correct prediction of the ESD disease. Conclusions: Results indicated that using of
this classifier could be useful. But, it would be strongly recommended that the combination of machine
learning methods could be more useful in terms of prediction of ESD.