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A Bio-Inspired-Based Salp Swarm Algorithm Enabled with Deep Learning for Alzheimer’s Classification

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Applied Informatics (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1874))

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Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder for which early identification is of paramount importance for a holistic treatment plan. Traditional methods of diagnosis require extensive manual interventions, making their scalability and reproducibility difficult. This paper presents a novel Bio-Inspired Salp Swarm Algorithm (BI-SSA) technique enabled by Deep Learning for the classification of Alzheimer’s disease. The social behavior of birds and insects served as inspiration for the optimization technique known as BI-SSA which is able to identify useful solutions to complex problems with minimum manual interventions. This paper extends BI-SSA using Deep Learning which enables it to generate a more accurate and reliable diagnostic model. The model incorporates Alzheimer’s disease-specific features such as age, gender, family history, and cognitive tests and employs an ensemble approach to improve the accuracy of the model. The proposed model is evaluated using a publicly available ADNI dataset. The results demonstrate that the model is able to correctly classify AD patients with an accuracy of 99.9%. Furthermore, our BI-SSA-based model outperforms traditional machine learning techniques and achieves better results with respect to sensitivity, precision, and accuracy of classification.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin, 240003, Nigeria

    Joseph Bamidele Awotunde

  2. First Technical University Ibadan, Ibadan, Nigeria

    Sunday Adeola Ajagbe

  3. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

Authors
  1. Joseph Bamidele Awotunde
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  2. Sunday Adeola Ajagbe
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  3. Hector Florez
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Corresponding author

Correspondence to Sunday Adeola Ajagbe .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Universidad Ecotec, Guayaquil, Ecuador

    Marcelo Leon

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Awotunde, J.B., Ajagbe, S.A., Florez, H. (2024). A Bio-Inspired-Based Salp Swarm Algorithm Enabled with Deep Learning for Alzheimer’s Classification. In: Florez, H., Leon, M. (eds) Applied Informatics. ICAI 2023. Communications in Computer and Information Science, vol 1874. Springer, Cham. https://doi.org/10.1007/978-3-031-46813-1_11

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  • DOI: https://doi.org/10.1007/978-3-031-46813-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46812-4

  • Online ISBN: 978-3-031-46813-1

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