Cerebral infarction classification using multiple support vector machine with information gain feature selection

Zuherman Rustam, Arfiani, Jacub Pandelaki

Research output: Contribution to journalArticle

Abstract

Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from CiptoMangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.

Original languageEnglish
Pages (from-to)1578-1584
Number of pages7
JournalBulletin of Electrical Engineering and Informatics
Volume9
Issue number4
DOIs
Publication statusPublished - Aug 2020

Keywords

  • Cerebral infarction
  • Information gain
  • Support vector machine

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