A Soft Computing Method for Mesothelioma Disease Classification

Mehrbakhsh Nilashi, Hossein Ahmadi, Leila Shahmoradi, Maryam Salahshour, Othman Ibrahim


Malignant Mesothelioma (MM) is a rare but highly aggressive tumour. The aim of this study is to improve the classification accuracy of MM disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use Expectation Maximization (EM), Principal Component Analysis (PCA) and Support Vector Machine (SVM) for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental PCA and incremental SVM for incremental learning of data. Experimental results on a malignant pleural mesothelioma disease dataset show that proposed method remarkably improves the accuracy of prediction and reduce computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.


Malignant mesothelioma, Clustering, Incremental PCA, Incremental SVM, Machine Learning

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