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

Jayashree Piri, Puspanjali Mohapatra, Biswaranjan Acharya, Farhad Soleimanian Gharehchopogh, Vassilis C. Gerogiannis, Andreas Kanavos and Stella Manika
Additional contact information
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

Mathematics, 2022, vol. 10, issue 15, 1-31

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.

Keywords: artificial gorilla troop optimization; biomedical data; COVID-19; feature selection; multi-objective optimization; single-objective optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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