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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283031/ https://www.ncbi.nlm.nih.gov/pubmed/35845952 http://dx.doi.org/10.1155/2022/2318101 |
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author | Saxena, Komal Zamani, Abu Sarwar Bhavani, R. Sagar, K. V. Daya Bangare, Pushpa M. Ashwini, S. Rahin, Saima Ahmed |
author_facet | Saxena, Komal Zamani, Abu Sarwar Bhavani, R. Sagar, K. V. Daya Bangare, Pushpa M. Ashwini, S. Rahin, Saima Ahmed |
author_sort | Saxena, Komal |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9283031 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92830312022-07-15 Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure Saxena, Komal Zamani, Abu Sarwar Bhavani, R. Sagar, K. V. Daya Bangare, Pushpa M. Ashwini, S. Rahin, Saima Ahmed Biomed Res Int Research Article 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. Hindawi 2022-07-07 /pmc/articles/PMC9283031/ /pubmed/35845952 http://dx.doi.org/10.1155/2022/2318101 Text en Copyright © 2022 Komal Saxena et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Saxena, Komal Zamani, Abu Sarwar Bhavani, R. Sagar, K. V. Daya Bangare, Pushpa M. Ashwini, S. Rahin, Saima Ahmed Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure |
title | Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure |
title_full | Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure |
title_fullStr | Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure |
title_full_unstemmed | Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure |
title_short | Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure |
title_sort | appropriate supervised machine learning techniques for mesothelioma detection and cure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283031/ https://www.ncbi.nlm.nih.gov/pubmed/35845952 http://dx.doi.org/10.1155/2022/2318101 |
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