Malignant Mesothelioma Disease Diagnosis using Data Mining Techniques

Malignant mesothelioma (MM) is very aggressive progress tumors of the pleura. MM in humans results from exposure to asbestos and asbestiform fibers. The incidence of MM is extremely high in some Turkish villages. Under computationally efficient data mining (DM) techniques, classification procedures were performed for MM disease diagnosis. The support vector machine (SVM) achieved promising results, outperforming the multilayer perceptron ensembles (MLPE) neural network method. It was observed that SVM is the best classification with 99.87% accuracy obtained via 10-fold crossvalidation in 5 runs when compare to MLPE neural network, which gives 99.56% classification accuracy. Sensitivity analysis is performed to find the important inputs for MM disease diagnosis under SVM model. Alkaline phosphatase (ALP) ranging from 300 to 500 gives the maximum possibility of having the MM disease. The MM disease dataset was prepared from a faculty of medicine’s database using new patient’s hospital reports from the south east region of Turkey.