SVM Classification in the Success of Wart Treatment Methods

NewsBot · Nov 25, 2018

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Evaluation of Kernel Effects on SVM Classification
in the Success of Wart Treatment Methods
Ali DEGIRMENCI and Omer KARAL
American Journal of Engineering Research (AJER)

Computer-aided systems have started to be used in the medical field as well as in many areas
over the last few decades. These studies are generally focused on the detection of the disease using medical
images. However, the determination of the treatment method to be applied is as important as the diagnosis of the
disease. Because, choosing the appropriate treatment method affects the success of the treatment, duration of the
healing period. There are several approaches to determining wart treatment methods. One of these is the support
vector machine (SVM), which is widely used in classification problems. However, the accuracy of the SVM
depends on the type and parameters of the kernel used. In this study, SVM based learning algorithm with
different kernels and parameters was investigated in predicting the success of wart treatment methods. In order
to obtain more robust and reliable results, the used kernels and their parameters have been analyzed by k-fold
cross-validation method. It was observed from the experimental results that SVM with Gaussian kernel (C=29
,
σ=24
) achieved 93,11% success in the "cryotherapy" data set and that SVM with linear kernel (C=2-2
) achieved
81,33% success in the "immunotherapy" data set. These rates are higher than previously reported. In conclusion,
the success of the proposed SVM based learning model is highly dependent on the kernel and parameters used.
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