<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>As in all fields, technological developments have started to be used in the field of medical diagnosis, and computer-aided diagnosis systems have started to assist physicians in their diagnosis. The success of computer-aided diagnosis methods depends on the method used; dataset, pre-processing, post-processing, etc. differ according to the processes. In this study, parameter optimization of support vector machines was performed with four different methods currently used in the literature to assist the physician in diagnosis. The success of each method was tested on two different Parkinson’s datasets and the results were compared within themselves and with the literature. According to the results obtained, the highest accuracy rates vary depending on the dataset and optimization method. While Improved Chaotic Particle Swarm Optimization achieved high success in the first dataset, Bat Algorithm achieved higher success in the other dataset. While the successful results obtained are better than some studies in the literature, they are at a level that can compete with some studies.</p>
</abstract>

As in all fields, technological developments have started to be used in the field of medical diagnosis, and computer-aided diagnosis systems have started to assist physicians in their diagnosis. The success of computer-aided diagnosis methods depends on the method used; dataset, pre-processing, post-processing, etc. differ according to the processes. In this study, parameter optimization of support vector machines was performed with four different methods currently used in the literature to assist the physician in diagnosis. The success of each method was tested on two different Parkinson’s datasets and the results were compared within themselves and with the literature. According to the results obtained, the highest accuracy rates vary depending on the dataset and optimization method. While Improved Chaotic Particle Swarm Optimization achieved high success in the first dataset, Bat Algorithm achieved higher success in the other dataset. While the successful results obtained are better than some studies in the literature, they are at a level that can compete with some studies.

Optimization of Support Vector Machines for Prediction of Parkinson’s Disease

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

As in all fields, technological developments have started to be used in the field of medical diagnosis, and computer-aided diagnosis systems have started to...

Optimization of Support Vector Machines for Prediction of Parkinson’s Disease

Data publikacji: 07 mar 2023

Zakres stron: 1 - 10

Otrzymano: 14 mar 2022

Przyjęty: 02 lut 2023

DOI: https://doi.org/10.2478/msr-2023-0001

Słowa kluczowe
Support vector machines, Parameter optimization, Classification, Parkinson’s disease, machine learning, acoustic analysis

© 2023 Turgut Özseven et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Optimization of Support Vector Machines for Prediction of Parkinson’s Disease

Data publikacji: 07 mar 2023

Zakres stron: 1 - 10

Otrzymano: 14 mar 2022

Przyjęty: 02 lut 2023

DOI: https://doi.org/10.2478/msr-2023-0001

Słowa kluczoweSupport vector machines, Parameter optimization, Classification, Parkinson’s disease, machine learning, acoustic analysis

© 2023 Turgut Özseven et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Support vector machines, Parameter optimization, Classification, Parkinson’s disease, machine learning, acoustic analysis