Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective
Immunotherapy treatments can be essential sometimes and a waste of valuable resources in other cases, depending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by exploring: application domains e.g warts, datasets e.g immunothera...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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FRUCT
2022-11-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/volume-32/fruct32/files/Mah.pdf |
| _version_ | 1828127000049483776 |
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| author | Ahsanullah Yunas Mahmoud Daniel Neagu Daniele Scrimieri Amr Rashad Ahmed Abdullatif |
| author_facet | Ahsanullah Yunas Mahmoud Daniel Neagu Daniele Scrimieri Amr Rashad Ahmed Abdullatif |
| author_sort | Ahsanullah Yunas Mahmoud |
| collection | DOAJ |
| description | Immunotherapy treatments can be essential sometimes and a waste of valuable resources in other cases, depending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by exploring: application domains e.g warts, datasets e.g immunotherapy, classifiers or algorithms e.g kNN, software tools e.g. python, and publications. The objective was to study the immunotherapy related published literature, from a supervised machine learning perspective. In addition, to reproduce research papers implementations of Random Forest and kNN among other algorithms. To find gaps and challenges both in publications and practical work, which may be the basis for further research. Immunotherapy, diabetes, cryotherapy, exasens data and one unbalanced dataset are explored. Random Forest performed better. The results are compared with published literature. To address the found gaps in further research: novel experiments, unbalanced studies, focus on effectiveness and a new algorithm or classifier are suggested. |
| first_indexed | 2024-04-11T15:43:51Z |
| format | Article |
| id | doaj.art-a2ca6fd3f5b4488299d99a75bdbecaef |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-04-11T15:43:51Z |
| publishDate | 2022-11-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-a2ca6fd3f5b4488299d99a75bdbecaef2022-12-22T04:15:42ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372022-11-0132115216110.23919/FRUCT56874.2022.9953853Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning PerspectiveAhsanullah Yunas Mahmoud0Daniel Neagu1Daniele Scrimieri2Amr Rashad Ahmed Abdullatif3University of Bradford, United KingdomUniversity of Bradford, United KingdomUniversity of Bradford, United KingdomUniversity of Bradford, United KingdomImmunotherapy treatments can be essential sometimes and a waste of valuable resources in other cases, depending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by exploring: application domains e.g warts, datasets e.g immunotherapy, classifiers or algorithms e.g kNN, software tools e.g. python, and publications. The objective was to study the immunotherapy related published literature, from a supervised machine learning perspective. In addition, to reproduce research papers implementations of Random Forest and kNN among other algorithms. To find gaps and challenges both in publications and practical work, which may be the basis for further research. Immunotherapy, diabetes, cryotherapy, exasens data and one unbalanced dataset are explored. Random Forest performed better. The results are compared with published literature. To address the found gaps in further research: novel experiments, unbalanced studies, focus on effectiveness and a new algorithm or classifier are suggested.https://www.fruct.org/publications/volume-32/fruct32/files/Mah.pdfimmunotherapycryotherapymachine learningclassificationrandom forestwarts |
| spellingShingle | Ahsanullah Yunas Mahmoud Daniel Neagu Daniele Scrimieri Amr Rashad Ahmed Abdullatif Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective Proceedings of the XXth Conference of Open Innovations Association FRUCT immunotherapy cryotherapy machine learning classification random forest warts |
| title | Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective |
| title_full | Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective |
| title_fullStr | Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective |
| title_full_unstemmed | Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective |
| title_short | Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective |
| title_sort | review of immunotherapy classification application domains datasets algorithms and software tools from machine learning perspective |
| topic | immunotherapy cryotherapy machine learning classification random forest warts |
| url | https://www.fruct.org/publications/volume-32/fruct32/files/Mah.pdf |
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