Diagnosis of Mesothelioma Disease Using Different Classification Techniques

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.

Identification of Malignant Mesothelioma Risk Factors through Association Rule Mining

“…With the rise of computing methods for disease prediction, data mining also played a vital rule to predict malignant mesothelioma. Several data mining models were utilized for early diagnosis of malignant mesothelioma [19,20]. Geographical, Socioeconomic and clinical factors were used for early prediction of mesothelioma with the help of data mining techniques [21,22].…”

“…Genetic algorithm+SSAE efficiently classified the dataset in terms of ROC curve, F-measure, accuracy, specificity, and sensitivity and given the best results. Kemal Tutuncu and Ozcan Cataltas [20] diagnosed mesothelioma disease by the utilization of different classification methods. 9 different types of classification methods were applied on the dataset which obtained from Dicle University, faculty of medicine.…”