Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.
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 p...
| Publicado en: | BioMed Research International pp. 1 - 7 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/27/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158209962&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158209962 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/27/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158209962 158209962 158209962 10.1155/2022/9900668 158209962 ppf: 1 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique. aug: au: Shobana, M. Balasraswathi, V. R. Radhika, R. Oleiwi, Ahmed Kareem Chaudhury, Sushovan Ladkat, Ajay S. Naved, Mohd Rahmani, Abdul Wahab affil: SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Kanchipuram, 603203, Chennai, India sug: subj: Mesothelioma Diagnosis Machine Learning Utilization Mesothelioma Classification Technology, Medical Algorithms ab: 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. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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