An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical Data: A COVID-19 Case Study

Feature Subset Selection (FSS) is an NP-hard problem to remove redundant and irrelevant features particularly from medical data, and it can be effectively addressed by metaheuristic algorithms. However, existing binary versions of metaheuristic algorithms have issues with convergence and lack an eff...

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Vydáno v:Journal of bionics engineering Ročník 21; číslo 1; s. 426 - 446
Hlavní autoři: Fatahi, Ali, Nadimi-Shahraki, Mohammad H., Zamani, Hoda
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Nature Singapore 01.01.2024
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Biochemical Engineering
Bioinformatics
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Convergence
COVID-19
Datasets
Effectiveness
Engineering
Heuristic methods
Navigation
Operators (mathematics)
Preprocessing
Research Article
Search methods
Transfer functions
Feature subset selection
Bioinspired
Binary metaheuristic algorithms
Medical datasets
Optimization
Machine learning
ISSN:1672-6529, 2543-2141
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Shrnutí:Feature Subset Selection (FSS) is an NP-hard problem to remove redundant and irrelevant features particularly from medical data, and it can be effectively addressed by metaheuristic algorithms. However, existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method, resulting in suboptimal solutions that hinder diagnosis and prediction accuracy. This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm (IBQANA) for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms. The proposed IBQANA’s contributions include the Hybrid Binary Operator (HBO) and the Distance-based Binary Search Strategy (DBSS). HBO is designed to convert continuous values into binary solutions, even for values outside the [0, 1] range, ensuring accurate binary mapping. On the other hand, DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence. By combining exploration and exploitation phases based on an adaptive probability function, DBSS effectively avoids local optima. The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets, with feature numbers ranging from 8 to 10,509. IBQANA's effectiveness is evaluated regarding the accuracy, fitness, and selected features and compared with seven binary metaheuristic algorithms. Furthermore, IBQANA is utilized to detect COVID-19. The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets. The proposed method presents a promising solution to the FSS problem in medical data preprocessing.
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ISSN:1672-6529
2543-2141
DOI:10.1007/s42235-023-00433-y