A Machine Learning Approach for Identification of Malignant Mesothelioma Etiological Factors in an Imbalanced Dataset

Abstract

Abstract In today’s world, lung cancer is a significant health burden, and it is one of the most leading causes of death. A leading type of lung cancer is malignant mesothelioma (MM). Most of the MM patients do not show any symptoms. Etiology plays a vital factor in the diagnosis of any disease. Positron emission tomography (PET), magnetic resonance imaging (MRI), biopsies, X-rays and blood tests are essential but costly and invasive MM risk factor identification methods. In this work, we mainly focused on the exploration of the MM risk factors. The identification of mesothelioma symptoms was carried out by utilizing the data of mesothelioma patients. However, the dataset was comprised of both healthy and mesothelioma patients. The dataset is prone to a class imbalance problem in which the number of MM patients significantly less than healthy individuals. To overcome the class imbalance problem, the synthetic minority oversampling technique has been utilized. The association rule mining-based Apriori algorithm has been applied to a preprocessed dataset. Before using the Apriori algorithm, both duplicate and irrelevant attributes were removed. Moreover, the numerical attributes were also classified into nominal attributes and the association rules were generated in the dataset. Our results show that erythrocyte sedimentation rate, asbestos exposure and its duration time, and pleural and serum lactic dehydrogenase ratio are major risk factors of MM. The severe stages of MM can be avoided by earlier identification of risk factors of the disease. The failure of identification of risk factors can lead to increased risk of multiple medical conditions, including cardiovascular diseases, mental distress, diabetes and anemia.