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    Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective

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    Accepted manuscript (151.3Kb)
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    Publication date
    2022-11
    Author
    Mahmoud, Ahsanullah Y.
    Neagu, Daniel
    Scrimieri, Daniel
    Abdullatif, Amr R.A.
    Keyword
    Immunotherapy
    Classification
    Machine learning
    Application domains
    Datasets
    Algorithms
    Software tools
    Rights
    © 2022 Open Innovations Association (FRUCT). Reproduced in accordance with the publisher's self-archiving policy.
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
    Metadata
    Show full item record
    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.
    URI
    http://hdl.handle.net/10454/19308
    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
    https://doi.org/10.23919/FRUCT56874.2022.9953853
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

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