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Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data

Author

Listed:
  • Jayashree Piri

    (Department of CSE, GITAM Institute of Technology (Deemed to be University), Visakhapatnam 530045, India)

  • Puspanjali Mohapatra

    (Department of CSE, International Institute of Information Technology, Bhubaneswar 751029, India)

  • Biswaranjan Acharya

    (Department of Computer Engineering-AI, Marwadi University, Rajkot 360003, India)

  • Farhad Soleimanian Gharehchopogh

    (Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia 5756151818, Iran)

  • Vassilis C. Gerogiannis

    (Department of Digital Systems, University of Thessaly, Geopolis Campus, 45100 Larissa, Greece)

  • Andreas Kanavos

    (Department of Digital Media and Communication, Ionian University, 28100 Kefalonia, Greece)

  • Stella Manika

    (Department of Planning and Regional Development, University of Thessaly, 38334 Volos, Greece)

Abstract

Feature selection (FS) is commonly thought of as a pre-processing strategy for determining the best subset of characteristics from a given collection of features. Here, a novel discrete artificial gorilla troop optimization (DAGTO) technique is introduced for the first time to handle FS tasks in the healthcare sector. Depending on the number and type of objective functions, four variants of the proposed method are implemented in this article, namely: (1) single-objective (SO-DAGTO), (2) bi-objective (wrapper) (MO-DAGTO1), (3) bi-objective (filter wrapper hybrid) (MO-DAGTO2), and (4) tri-objective (filter wrapper hybrid) (MO-DAGTO3) for identifying relevant features in diagnosing a particular disease. We provide an outstanding gorilla initialization strategy based on the label mutual information (MI) with the aim of increasing population variety and accelerate convergence. To verify the performance of the presented methods, ten medical datasets are taken into consideration, which are of variable dimensions. A comparison is also implemented between the best of the four suggested approaches (MO-DAGTO2) and four established multi-objective FS strategies, and it is statistically proven to be the superior one. Finally, a case study with COVID-19 samples is performed to extract the critical factors related to it and to demonstrate how this method is fruitful in real-world applications.

Suggested Citation

  • Jayashree Piri & Puspanjali Mohapatra & Biswaranjan Acharya & Farhad Soleimanian Gharehchopogh & Vassilis C. Gerogiannis & Andreas Kanavos & Stella Manika, 2022. "Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data," Mathematics, MDPI, vol. 10(15), pages 1-31, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2742-:d:879125
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    References listed on IDEAS

    as
    1. Ahmed Ginidi & Sherif M. Ghoneim & Abdallah Elsayed & Ragab El-Sehiemy & Abdullah Shaheen & Attia El-Fergany, 2021. "Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    2. Ahmed Majid Taha & Soong-Der Chen & Aida Mustapha, 2015. "Bat Algorithm Based Hybrid Filter-Wrapper Approach," Advances in Operations Research, Hindawi, vol. 2015, pages 1-5, October.
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    Cited by:

    1. Jinhua You & Heming Jia & Di Wu & Honghua Rao & Changsheng Wen & Qingxin Liu & Laith Abualigah, 2023. "Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 11(5), pages 1-42, March.
    2. Mohammad H. Nadimi-Shahraki & Hoda Zamani & Ali Fatahi & Seyedali Mirjalili, 2023. "MFO-SFR: An Enhanced Moth-Flame Optimization Algorithm Using an Effective Stagnation Finding and Replacing Strategy," Mathematics, MDPI, vol. 11(4), pages 1-28, February.
    3. Adrian Marius Deaconu & Daniel Tudor Cotfas & Petru Adrian Cotfas, 2023. "Advanced Optimization Methods and Applications," Mathematics, MDPI, vol. 11(9), pages 1-7, May.

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