Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure.
Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, lin...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/7/2022
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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=157865087&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157865087 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/7/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157865087 157865087 157865087 10.1155/2022/2318101 157865087 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure. aug: au: Saxena, Komal Zamani, Abu Sarwar Bhavani, R. Sagar, K. V. Daya Bangare, Pushpa M. Ashwini, S. Rahin, Saima Ahmed affil: Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India sug: subj: Machine Learning Utilization Mesothelioma Diagnosis Mesothelioma Therapy Learning Methods Supervisors and Supervision Human Logistic Regression Decision Support Systems, Clinical Algorithms Cluster Analysis Discriminant Analysis Radiography, Thoracic ab: Mesothelioma is a dangerous, violent cancer, which forms a protecting layer around inner tissues such as the lungs, stomach, and heart. We investigate numerous AI methodologies and consider the exact DM conclusion outcomes in this study, which focuses on DM determination. K-nearest neighborhood, linear-discriminant analysis, Naive Bayes, decision-tree, random forest, support vector machine, and logistic regression analyses have been used in clinical decision support systems in the detection of mesothelioma. To test the accuracy of the evaluated categorizers, the researchers used a dataset of 350 instances with 35 highlights and six execution measures. LDA, NB, KNN, SVM, DT, LogR, and RF have precisions of 65%, 70%, 92%, 100%, 100%, 100%, and 100%, correspondingly. In count, the calculated complication of individual approaches has been evaluated. Every process is chosen on the basis of its characterization, exactness, and calculated complications. SVM, DT, LogR, and RF outclass the others and, unexpectedly, earlier research. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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