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Salp Chain-Based Optimization of Support Vector Machines and Feature Weighting for Medical Diagnostic Information Systems

  • Ala’ M. Al-Zoubi
  • Ali Asghar Heidari
  • Maria Habib
  • Hossam FarisEmail author
  • Ibrahim Aljarah
  • Mohammad A. Hassonah
Chapter
Part of the Algorithms for Intelligent Systems book series (AIS)

Abstract

Nowadays, medical diagnosis based on machine learning is an essential, active, and interdisciplinary research area. Making smart diagnosis and decision support systems have a profound impact on healthcare informatics. Integrating machine learning classifier systems into computer-aided diagnosis systems promotes the early detection of diseases, which results in more effective treatments and prolonged survival. In this chapter, we address popular diagnosis problems using an evolutionary machine learning approach which performs feature weighting and tuning the parameters of support vector machines (SVMs) simultaneously. A new and powerful metaheuristic called salp swarm algorithm is combined with SVM for this task. The designed SSA-SVM approach shows several merits compared to other SVM-based frameworks with well-regarded algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO).

Keywords

Medical diagnostic Machine learning SVM SSA Feature weighting 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ala’ M. Al-Zoubi
    • 1
  • Ali Asghar Heidari
    • 2
    • 3
  • Maria Habib
    • 1
  • Hossam Faris
    • 1
    Email author
  • Ibrahim Aljarah
    • 1
  • Mohammad A. Hassonah
    • 1
  1. 1.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  2. 2.School of Surveying and Geospatial Engineering, College of EngineeringUniversity of TehranTehranIran
  3. 3.Department of Computer Science, School of ComputingNational University of SingaporeSingaporeSingapore

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