Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique. (27th July 2022)

• Record Type: Journal Article
• Authors: Shobana, M.; Balasraswathi, V. R.; Radhika, R.; Oleiwi, Ahmed Kareem; Chaudhury, Sushovan; Ladkat, Ajay S.; Naved, Mohd; Rahmani, Abdul Wahab
• Other Names: Kaur Gaganpreet Academic Editor.
• Is Part Of: BioMed research international. Volume 2022(2022)
• Publication Date: 2022-07-27
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2022/9900668
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always results in death. Chemotherapy, surgery, radiation therapy, and immunotherapy are all potential treatments for multiple myeloma; however, the majority of patients are identified with the disease at an advanced stage, at which time it is resistant to these therapies. After obtaining a diagnosis of advanced multiple myeloma, the average length of time that a person lives is one year after hearing this news. There is a substantial link between asbestos exposure and mesothelioma (MM). Using an approach that enables feature selection and machine learning, this article proposes a classification and detection method for mesothelioma cancer. The CFS correlation-based feature selection approach is first used in the feature selection process. It acts as a filter, selecting just the traits that are relevant to the categorization. The accuracy of the categorization model is improved as a direct consequence of this. After that, classification is carried out with the help of naive Bayes, fuzzy SVM, and the ID3 algorithm. Various metrics have been utilized during the process of measuring the effectiveness of machine learning strategies. It has been discovered that the choice of features has a substantial influence on the accuracy of the categorization.