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All Journal International Journal of Intelligent Systems and Applications in Engineering
Cataltas, Ozcan
Advanced Technology and Science (ATScience)
Computer Science & IT

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Diagnosis of Mesothelioma Disease Using Different Classification Techniques Tutuncu, Kemal; Cataltas, Ozcan
International Journal of Intelligent Systems and Applications in Engineering 2017: Special Issue
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017SpecialIssue31416

Abstract

Mesothelioma, which is a disease of the pleura and peritoneum, is an asbestos-related environmental disease in undeveloped countries. Although the incidence of this disease is lower than that of lung cancer, the reaction it creates in society is very high. In this study, 9 different classification algorithms of data mining were applied to the Mesethelioma data set obtained from real patients in Dicle University, Faculty of Medicine and loaded into UCI Machine Learning Repository, and the results were compared. When the obtained results were examined, it has been seen that Artificial Neural Network (ANN) had %99.0740 correct classification ratio. 
Co-Authors Tutuncu, Kemal