The Mesothelioma Disease Diagnosis with Artificial Intelligence Methods - Ilhan, Hamza Osman; Celik, Enes (IEEE, 2016)
Asbestos is a carcinogenic substance, and threatens human health. Malignant Mesothelioma disease is one of the most dangerous kind of cancer caused by asbestos mineral. The most common symptom of the disease, progressive shortness of breath and constant pain. Early treatment and diagnosis are necessary. Otherwise, the disease can lead people to die in a short period of time. In this paper, different types of artificial intelligence methods are compared for effective Malignant Mesothelioma's diseases classification. Support Vector Machine, Neural Network and Decision Tree methods are selected in terms of regular machine learning concept. Additionally, Bagging and Adaboost re-sampling within ensemble learning terminology is also adapted. Totally 324 Malignant Mesothelioma data which consists of 34 features is used in this study. K-fold cross-validation technique is performed to compute the performance of the algorithms with different K values. 100% classification accuracies are obtained from three tested methods; Support Vector Machine, Decision Tree and Bagging. Additionally, the process time of methods are measured in case of using method in lots of data. In this sense, methods are evaluated based on accuracy and time complexity. The results of this paper are also compared with previous studies using same Malignant Mesothelioma's dataset.
Keywords: Malignant Mesothelioma; Support Vector Machine; Decision Tree; Neural Network; Ensemble Learning; Computer Science