title: Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree.
abstract: BACKGROUND
  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% (.
  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.
authors.affiliation: Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.
authors.forename: Keivan
authors.initials: K
authors.lastname: Maghooli
authors.affiliation: Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
authors.forename: Mostafa
authors.initials: M
authors.lastname: Langarizadeh
authors.affiliation: Department of Health Information Management, Faculty of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Leila
authors.initials: L
authors.lastname: Shahmoradi
authors.affiliation: Department of Health Information Management, Faculty of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran; Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Habibi-Koolaee
authors.affiliation: Department of Librarianship and Medical Information, Hamadan University of Medical Sciences& Health Services, Hamadan, Iran.
authors.forename: Mohamad
authors.initials: M
authors.lastname: Jebraeily
authors.affiliation: Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Hamid
authors.initials: H
authors.lastname: Bouraghi
keywords: Classification and Regression Tree; Erythmato-Squamous Diseases; classification; classifier; data mining; dermatology