Machine Learning Algorithm Selection for a Clinical Decision Support System Based on a Multicriteria Method

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

On the current information in the medical area related to cancer analysis, the selection of an optimal Machine Learning algorithm, based on a multicriteria method, for a system that supports clinical decisions is sought. As a methodology, exploratory research and the deductive method were applied to analyze the information from existing articles and ML algorithms' behavior applied in the area of medicine. This research and based on a use case of training and testing of the GLM, SVM, and ANN algorithms for selecting an algorithm. Addition-ally, for clinical decisions, and architecture prototype for medical data collection is presented resulted. Based on AHP and TOPSIS methods Support Vector Machine (SVM) is the best alternative.

Original languageEnglish
Title of host publicationHuman Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET
Subtitle of host publicationFuture Systems IHIET-FS 2021
EditorsTareq Ahram, Redha Taiar
PublisherSpringer Science and Business Media Deutschland GmbH
Pages1002-1010
Number of pages9
ISBN (Print)9783030855390
DOIs
StatePublished - 2022
Event5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021 - Virtual, Online
Duration: 27 Aug 202129 Aug 2021

Publication series

NameLecture Notes in Networks and Systems
Volume319
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021
CityVirtual, Online
Period27/08/2129/08/21

Bibliographical note

Funding Information:
This work has been supported by the GIIAR research group and the Universidad Polit?cnica Salesiana.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Decision clinical
  • Machine Learning
  • Medical data
  • Multicriteria method
  • Support aystem

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