Feature Selection and Classification Using CatBoost Method for Improving the Performance of Predicting Parkinson’s Disease

Mohammed Al-Sarem*, Faisal Saeed, Wadii Boulila, Abdel Hamid Emara, Muhannad Al-Mohaimeed, Mohammed Errais

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    34 Citations (SciVal)
    Original languageEnglish
    Title of host publicationAdvances on Smart and Soft Computing - Proceedings of ICACIN 2020
    EditorsFaisal Saeed, Tawfik Al-Hadhrami, Fathey Mohammed, Errais Mohammed
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages189-199
    Number of pages11
    ISBN (Print)9789811560477
    DOIs
    Publication statusPublished (VoR) - 2021
    Event1st International Conference of Advanced Computing and Informatics, ICACIN 2020 - Casablanca, Morocco
    Duration: 13 Apr 202014 Apr 2020

    Publication series

    NameAdvances in Intelligent Systems and Computing
    Volume1188
    ISSN (Print)2194-5357
    ISSN (Electronic)2194-5365

    Conference

    Conference1st International Conference of Advanced Computing and Informatics, ICACIN 2020
    Country/TerritoryMorocco
    CityCasablanca
    Period13/04/2014/04/20

    Keywords

    • CatBoost method
    • Ensemble methods
    • Feature selection
    • Features importance
    • Parkinson’s disease

    Fingerprint

    Dive into the research topics of 'Feature Selection and Classification Using CatBoost Method for Improving the Performance of Predicting Parkinson’s Disease'. Together they form a unique fingerprint.

    Cite this