IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i15p2742-d879125.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/15/2742/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/15/2742/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ashraf K. Abdelaal & Elshahat F. Mohamed & Attia A. El-Fergany, 2022. "Optimal Scheduling of Hybrid Sustainable Energy Microgrid: A Case Study for a Resort in Sokhna, Egypt," Sustainability, MDPI, vol. 14(19), pages 1-13, October.
    2. Xinghua Liu & Siwei Qiao & Zhiwei Liu, 2023. "A Survey on Load Frequency Control of Multi-Area Power Systems: Recent Challenges and Strategies," Energies, MDPI, vol. 16(5), pages 1-22, February.
    3. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Martin Calasan & Mihailo Micev & Ziad M. Ali & Saad Mekhilef & Hussain Bassi & Hatem Sindi & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer," Mathematics, MDPI, vol. 10(7), pages 1-31, March.
    4. Hegazy Rezk & A. G. Olabi & Tabbi Wilberforce & Enas Taha Sayed, 2023. "A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    5. Ragab El-Sehiemy & Abdallah Elsayed & Abdullah Shaheen & Ehab Elattar & Ahmed Ginidi, 2021. "Scheduling of Generation Stations, OLTC Substation Transformers and VAR Sources for Sustainable Power System Operation Using SNS Optimizer," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    6. Adel A. Abou El-Ela & Ragab A. El-Sehiemy & Abdullah M. Shaheen & Aya R. Ellien, 2022. "Review on Active Distribution Networks with Fault Current Limiters and Renewable Energy Resources," Energies, MDPI, vol. 15(20), pages 1-30, October.
    7. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
    8. El-Dabah, Mahmoud A. & El-Sehiemy, Ragab A. & Hasanien, Hany M. & Saad, Bahaa, 2023. "Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm," Energy, Elsevier, vol. 262(PB).
    9. Shaheen, Abdullah M. & Ginidi, Ahmed R. & El-Sehiemy, Ragab A. & El-Fergany, Attia & Elsayed, Abdallah M., 2023. "Optimal parameters extraction of photovoltaic triple diode model using an enhanced artificial gorilla troops optimizer," Energy, Elsevier, vol. 283(C).
    10. Abdullah Shaheen & Ahmed Ginidi & Ragab El-Sehiemy & Abdallah Elsayed & Ehab Elattar & Hassen T. Dorrah, 2022. "Developed Gorilla Troops Technique for Optimal Power Flow Problem in Electrical Power Systems," Mathematics, MDPI, vol. 10(10), pages 1-29, May.
    11. Ahmed Amin & Mohamed Ebeed & Loai Nasrat & Mokhtar Aly & Emad M. Ahmed & Emad A. Mohamed & Hammad H. Alnuman & Amal M. Abd El Hamed, 2022. "Techno-Economic Evaluation of Optimal Integration of PV Based DG with DSTATCOM Functionality with Solar Irradiance and Loading Variations," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    12. Hossam Hassan Ali & Mohamed Ebeed & Ahmed Fathy & Francisco Jurado & Thanikanti Sudhakar Babu & Alaa A. Mahmoud, 2023. "A New Hybrid Multi-Population GTO-BWO Approach for Parameter Estimation of Photovoltaic Cells and Modules," Sustainability, MDPI, vol. 15(14), pages 1-33, July.
    13. Mostafa Elshahed & Ali M. El-Rifaie & Mohamed A. Tolba & Ahmed Ginidi & Abdullah Shaheen & Shazly A. Mohamed, 2022. "An Innovative Hunter-Prey-Based Optimization for Electrically Based Single-, Double-, and Triple-Diode Models of Solar Photovoltaic Systems," Mathematics, MDPI, vol. 10(23), pages 1-22, December.
    14. Ashraf Ramadan & Mohamed Ebeed & Salah Kamel & Ahmed M. Agwa & Marcos Tostado-Véliz, 2022. "The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer," Energies, MDPI, vol. 15(4), pages 1-22, February.
    15. Abdelmonem Draz & Mahmoud M. Elkholy & Attia A. El-Fergany, 2023. "Automated Settings of Overcurrent Relays Considering Transformer Phase Shift and Distributed Generators Using Gorilla Troops Optimizer," Mathematics, MDPI, vol. 11(3), pages 1-25, February.
    16. Jiang, Meng & Ding, Kun & Chen, Xiang & Cui, Liu & Zhang, Jingwei & Cang, Yi & Yang, Hang & Gao, Ruiguang, 2024. "CGH-GTO method for model parameter identification based on improved grey wolf optimizer, honey badger algorithm, and gorilla troops optimizer," Energy, Elsevier, vol. 296(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2742-:d:879125. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.