Open AccessTwin support vector machine using kernel function for colorectal cancer detection
Author(s) -
Zuherman Rustam,
Fildzah Zhafarina,
Jane Eva Aurelia,
Yasirly Amalia
Publication year - 2021
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v10i6.3179
Subject(s) - support vector machine , colorectal cancer , polynomial kernel , artificial intelligence , kernel (algebra) , polynomial , computer science , machine learning , gaussian function , field (mathematics) , algorithm , cancer , kernel method , gaussian , mathematics , medicine , discrete mathematics , pure mathematics , mathematical analysis , physics , quantum mechanics
Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of articial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.