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Swarm Intelligence Algorithms-Based Machine Learning Framework for Medical Diagnosis: A Comprehensive Review

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Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1038))

Abstract

When building medical diagnosis software, one of the most difficult challenges in disease prediction. Machine Learning (ML) approaches have proven to be effective in a range of applications, including medical diagnostics. ML applications that require a high level of speed and accuracy are becoming more frequent. With these applications, the curse of dimensionality, in which the number of features exceeds the number of patterns, is a serious issue. One of the dimensionality reduction strategies that might increase task accuracy while reducing computational complexity is Feature Selection (FS). The goal of the FS method is to locate a subset of features that have the least inner similarity and are most relevant to the target class. By building classifier systems that can help clinicians forecast and detect diseases at an early stage, ML algorithms can aid in the solution of health-related problems. Swarm Intelligence (SI) algorithms are utilized in the detection and treatment of diseases. We can increase the reliability, performance, accuracy, and speed of diagnosing disease on the existing system by merging ML and SI methods. The goal of this paper is to use ML and SI to diagnose diseases. This paper discusses various recently discovered SI and ML algorithms that have been effectively applied to a range of optimization problems, focusing on their implementation areas, strengths, and weaknesses.

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  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam Halim Houssein, Eman Saber, Yaser M. Wazery & Abdelmgeid A. Ali

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  2. Eman Saber
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  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam Halim Houssein

  2. Faculty of Computer Science & Engineering, Galala University, Suze, Egypt

    Mohamed Abd Elaziz

  3. Department of Computer Sciences, University of Guadalajara, Guadalajara, Jalisco, Mexico

    Diego Oliva

  4. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

    Laith Abualigah

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Houssein, E.H., Saber, E., Wazery, Y.M., Ali, A.A. (2022). Swarm Intelligence Algorithms-Based Machine Learning Framework for Medical Diagnosis: A Comprehensive Review. In: Houssein, E.H., Abd Elaziz, M., Oliva, D., Abualigah, L. (eds) Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems. Studies in Computational Intelligence, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-99079-4_4

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