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Swarm Intelligence and Evolutionary Algorithms in Processing Healthcare Data

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Connected e-Health

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1021))

Abstract

Healthcare system in research in recent years has been in the frontline of research, and researchers has been tried to create several artificial intelligence (AI) models to solving the difficulties associated with medical diagnosis, prediction and forecast of medical data. Among such AI methods are Swarm Intelligence (SI) and Evolutionary Algorithms (EA) algorithms. These algorithms have brought rapid development in data analytics techniques driven by growth in healthcare data availability. The SI and EA encompass collective behavioral study in decentralized systems that involves computations for solving complex problems. SI provides derivative-free optimization, flexible, robust and easy to implement at low cost. SI with EAs are effective global optimization techniques that are very useful in medical system for features selections. There are research innovations on SI and EA techniques and applications in healthcare domain. Therefore, this paper presents an overview of SI and EA as applied to problems in the healthcare systems to processing the healthcare data with practical applications and techniques. The applications of both SI and EA in healthcare systems has occasioned in processing healthcare data were discussed. The results shown that the use of SI and EA applications and techniques are limited compared to similar artificial intelligence algorithms even with the numerous inherent benefits that lies in the optimization potentials of combined them. Since processing healthcare data is significant to diagnosis, treatments, medication, screening and ultimately reduce mortality rate, there is a need to extend research innovations in the area of SI and EA techniques in processing healthcare data.

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Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde & Rasheed Gbenga Jimoh

  2. Department of Computer Science, Landmark University, Omu-Aran, Nigeria

    Abidemi Emmanuel Adeniyi

  3. Department of Computer Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria

    Sunday Adeola Ajagbe

  4. KIET Group of Institutions, Ghaziabad, 201206, Delhi-NCR, India

    Akash Kumar Bhoi

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  1. Joseph Bamidele Awotunde
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  5. Akash Kumar Bhoi
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Correspondence to Joseph Bamidele Awotunde .

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Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Sushruta Mishra

  2. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Alfonso González-Briones

  3. KIET Group of Institutions, Ghaziabad, Delhi-NCR, India

    Akash Kumar Bhoi

  4. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  5. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Juan M. Corchado

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Awotunde, J.B., Adeniyi, A.E., Ajagbe, S.A., Jimoh, R.G., Bhoi, A.K. (2022). Swarm Intelligence and Evolutionary Algorithms in Processing Healthcare Data. In: Mishra, S., González-Briones, A., Bhoi, A.K., Mallick, P.K., Corchado, J.M. (eds) Connected e-Health. Studies in Computational Intelligence, vol 1021. Springer, Cham. https://doi.org/10.1007/978-3-030-97929-4_5

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