Neural Network-Support Vector Machine for Sinusitis Classification

Jane Eva Aurelia, Zuherman Rustam, Afifah Rofi Laeli, Faisa Maulidina

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

Abstract

Sinusitis is a common health problem, especially in childhood and adolescence, caused by the inflammation of the air-filled sinus cavities in the skull. In the early stages, sinusitis does not present any symptoms from the findings and diagnoses in the health sector. Therefore, this research used a machine learning method by applying different forms such as the Neural Network-Support Vector Machine. Neural Network is a popular method with wide range of applications, and known for its high accuracy value. Moreover, there is a Support Vector Machines with several kernel functions that is commonly used in the classification of diseases, and also known for its high accuracy value. This research discusses the combination of Neural Network-Support Vector Machine as classifier for the categorization of sinusitis. Therefore, the combination of NN-SVM with several linear kernels function can be compared as classifier and used by health sector for efficient diagnosis.

Original languageEnglish
Title of host publication2020 International Conference on Decision Aid Sciences and Application, DASA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1185-1189
Number of pages5
ISBN (Electronic)9781728196770
DOIs
Publication statusPublished - 8 Nov 2020
Event2020 International Conference on Decision Aid Sciences and Application, DASA 2020 - Virtual, Sakheer, Bahrain
Duration: 7 Nov 20209 Nov 2020

Publication series

Name2020 International Conference on Decision Aid Sciences and Application, DASA 2020

Conference

Conference2020 International Conference on Decision Aid Sciences and Application, DASA 2020
CountryBahrain
CityVirtual, Sakheer
Period7/11/209/11/20

Keywords

  • Classification
  • Machine Learning
  • Neural Network
  • Sinusitis
  • Support Vector Machine

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