| Issue |
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
|
|
|---|---|---|
| Article Number | 01026 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/itmconf/20257901026 | |
| Published online | 08 October 2025 | |
Knee Osteoarthritis Severity Classification using SVM
1 Master of Computer Applications, PES College of Engineering, Mandya, India
2 Department of Computer Science and Engineering, PES College of Engineering, Mandya, India
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Numerous ailments have adversely affected people's lifestyles in recent years. Among these, osteoarthritis (OA) of the knee has been identified as the main source of disability and restriction of activity, especially in senior citizens. It is essential to create fast and cost-efficient methods for diagnosing knee OA. In this paper, we analyze severity of OA in knee X-ray images by employing two machine learning (ML) models: decision tree (DT), as well as support vector machine (SVM). For capturing characteristics of the knee, we utilized histogram of oriented gradients (HOG) technique. Classifiers benefit from an enriched feature space. According to empirical findings, the SVM classifier achieved a 70% accuracy rate, outperforming the other.
© The Authors, published by EDP Sciences, 2025
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