Yes / 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 and
software tools. The research objectives were: 1) to study the
immunotherapy-related published literature from a supervised
machine learning perspective. In addition, to reproduce immunotherapy classifiers reported in research papers. 2) 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. 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 classifier
algorithm are suggested.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19308 |
| Date | 13 December 2022 |
| Creators | Mahmoud, Ahsanullah Y., Neagu, Daniel, Scrimieri, Daniele, Abdullatif, Amr R.A. |
| Publisher | Open Innovations Association (FRUCT) |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Conference paper, Accepted manuscript |
| Rights | © 2022 Open Innovations Association (FRUCT). Reproduced in accordance with the publisher's self-archiving policy., Unspecified |
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