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Binary Hybrid Artificial Hummingbird with Flower Pollination Algorithm for Feature Selection in Parkinson’s Disease Diagnosis

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Abstract

Parkinson’s disease is a neurodegenerative disorder that inflicts irreversible damage on humans. Some experimental data regarding Parkinson’s patients are redundant and irrelevant, posing significant challenges for disease detection. Therefore, there is a need to devise an effective method for the selective extraction of disease-specific information, ensuring both accuracy and the utilization of fewer features. In this paper, a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm (FPA), called BFAHA, is proposed to solve the problem of Parkinson’s disease diagnosis based on speech signals. First, combining FPA with Artificial Hummingbird Algorithm (AHA) can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA, such as premature convergence and easy falling into local optimum. Second, the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration, if the difference is too significant, the cross-mutation strategy in the genetic algorithm (GA) is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal solution. Finally, an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection (FS) tasks. In this paper, 10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson’s disease diagnosis. Compared with other state-of-the-art algorithms, BFAHA shows excellent competitiveness in both the test datasets and the classification problem, indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. U21A20464, 62066005, and by the Innovation Project of Guangxi Graduate Education under Grant No. YCSW2023259.

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Authors and Affiliations

  1. College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China

    Liuyan Feng, Yongquan Zhou & Qifang Luo

  2. Xiangsihu College of Guangxi University for Nationalities, Nanning, 532100, Guangxi, China

    Yongquan Zhou

  3. Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

    Yongquan Zhou & Qifang Luo

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LF investigation, experiment, writing—draft; YZ supervision, algorithm design and analysis, writing—review, QL writing—review and editing.

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Feng, L., Zhou, Y. & Luo, Q. Binary Hybrid Artificial Hummingbird with Flower Pollination Algorithm for Feature Selection in Parkinson’s Disease Diagnosis. J Bionic Eng 21, 1003–1021 (2024). https://doi.org/10.1007/s42235-023-00478-z

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