This is not the latest version of this item. The latest version can be found here.
Loading...
Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective
Mahmoud, Ahsanullah Y. Neagu, Daniel Abdullatif, Amr R.A.
Mahmoud, Ahsanullah Y.
Neagu, Daniel
Abdullatif, Amr R.A.
Publication Date
2022-11
End of Embargo
Supervisor
Rights
© 2022 Open Innovations Association (FRUCT). Reproduced in accordance with the publisher's self-archiving policy.
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2022-10-07
Institution
Department
Awarded
Embargo end date
Additional title
Abstract
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.
Version
Accepted manuscript
Citation
Mahmoud AY, Neagu D, Scrimieri D et al (2022) Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective. 32nd Conference of Open Innovations Association (FRUCT). 9-11 Nov, Tampere, Finland.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Conference paper