Linear Support Vector Machine and Logistic Regression for Cerebral Infarction Classification

Alva Andhika Sa'id, Zuherman Rustam, Velery Virgina Putri Wibowo, Qisthina Syifa Setiawan, Afifah Rofi Laeli

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

1 Citation (Scopus)

Abstract

Stroke, as one of Global Burden Disease (GBD), obstructing the flow of blood to the brain and neurologic devastation, comprises of two types, namely hemorrhagic and ischemic with approximately 87% of all strokes classified as ischemic due to cerebral infarction or the occlusion of a cerebral vessel. Therefore, early identification is needed to enable patients to obtain the right treatment and prevent chronic cerebral infarction. This research proposes the use of a machine learning algorithm for appropriate and early diagnosis of patients with cerebral infarction by comparing linear function kernel of Support Vector Machine (SVM) and logistic regression methods. The main advantage of this method is its ability to determine the best linear classifier between these two methods for cerebral infarction classification in four criteria, namely accuracy, precision, recall, and F1 score. The highest average accuracy and F1 score were used to determine the best classifier. The result showed that the linear function kernel of support vector machine is the best for cerebral infarction classification with 90.96% and 91.44% average of accuracy and F1 score, respectively. In conclusion, future studies need to be carried out to improve machine learning classification for medical diagnosis using a linear classifier.

Original languageEnglish
Title of host publication2020 International Conference on Decision Aid Sciences and Application, DASA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages827-831
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

  • Cerebral Infarction
  • Classification
  • Linear Kernel
  • Logistic Regression
  • Machine Learning
  • Support Vector Machine

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