Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm

DENIZ, A., KIZILOZ, Hakan, SEVINC, E. and DOKEROGLU, T. (2022). Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm. Expert Systems, 39 (5): e12949.

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Official URL: https://onlinelibrary.wiley.com/doi/10.1111/exsy.1...
Link to published version:: https://doi.org/10.1111/exsy.12949
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    Abstract

    The COVID-19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID-19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi-threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG-ELM) to predict the severity level of the COVID-19 patients. We conduct a set of experiments on a recently published real-world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi-threaded implementation with statistical analysis. In order to verify the efficiency of MG-ELM, we compare our results with traditional and state-of-the-art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy.

    Item Type: Article
    Uncontrolled Keywords: Artificial Intelligence & Image Processing; 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Sciences
    Identification Number: https://doi.org/10.1111/exsy.12949
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 04 Aug 2022 10:09
    Last Modified: 04 Aug 2022 10:09
    URI: https://shura.shu.ac.uk/id/eprint/30555

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