A Predictive Method for Mesothelioma Disease Classification Using Naïve Bayes Classifier

Mehrbakhsh Nilashi, Morteza Zamani Roudbaraki, Mohammadreza Farahmand


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 and classification approaches. Accordingly, we use Expectation Maximization (EM) and Naive Bayes (NB) for clustering and classification tasks, respectively. Experimental results on a malignant pleural mesothelioma disease dataset show that proposed method remarkably improves the accuracy of MM disease prediction. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.


Malignant Mesothelioma disease diagnosis, Clustering, Naïve Bayes, Machine learning

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