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Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree Publisher



Maghooli K1 ; Langarizadeh M2 ; Shahmoradi L3 ; Habibikoolaee M3, 4, 5 ; Jebraeily M6 ; Bouraghi H7
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Authors Affiliations
  1. 1. Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
  2. 2. Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
  3. 3. Department of Health Information Management, Faculty of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Health Management and Social Development Research Center, Golestan University of Medical Sciences, Gorgan, Iran
  5. 5. Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran
  6. 6. Department of Librarianship and Medical Information, Hamadan University of Medical Sciences and Health Services, Hamadan, Iran
  7. 7. Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

Source: Acta Informatica Medica Published:2016


Abstract

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. © 2016 Keivan Maghooli, Mostafa Langarizadeh, Leila Shahmoradi, Mahdi Habibi-koolaee, Mohamad Jebraeily, and Hamid Bouraghi.