Comparison between stochastic support vector machine (stochastic SVM) and Fuzzy Kernel Robust C-Means (FKRCM) in breast cancer classification

Rustam, Zuherman; Putri, Rievanda

doi:10.1063/1.5132475

Cited by 1 publication

(1 citation statement)

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“…Machine learning has been accepted for various classification problems in various fields, one of which is in the field of medicine. In the field of medicine, several machine learning methods are used to detect several types of cancer, namely breast cancer [8][9][10][11], cervical cancer [12,13], ovarian cancer [14,15], colon cancer [16], prostate cancer [17], and lung cancer [18].…”

Section: Introductionmentioning

confidence: 99%

Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

et al. 2020

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Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

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