The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production

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Ivan Izonin 1,* Andriy Trostianchyn 2 Zoia Duriagina 2 Roman Tkachenko 2 Tetiana Tepla 2 Nataliia Lotoshynska 2

1. Lviv Polytechnic National University, PIT department, Lviv, 70913, Ukraine

2. Lviv Polytechnic National University, Lviv, 70913, Ukraine

* Corresponding author.


Received: 16 Jun. 2018 / Revised: 15 Jul. 2018 / Accepted: 24 Jul. 2018 / Published: 8 Sep. 2018

Index Terms

Machine learning, classification, medical implants, Wiener polynomial, SVM, titanium allows


This document presents two developed methods for solving the classification task of medical implant materials based on the compatible use of the Wiener Polynomial and SVM. The high accuracy of the proposed methodology for solving this task are experimentally confirmed. A comparison of the proposed methods with existing ones: Logistic Regression; Linear SVC; Random Forest; SVC (linear kernel); SVC (RBF kernel); Random Forest + Wiener Polynomial is carried out. The duration of training of all methods that described in work is investigated. The article presents the visualization of all method results for solving this task.

Cite This Paper

Ivan Izonin, Andriy Trostianchyn, Zoia Duriagina, Roman Tkachenko, Tetiana Tepla, Nataliia Lotoshynska, "The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.9, pp.40-47, 2018. DOI:10.5815/ijisa.2018.09.05


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