Khairunnisa Ibrahim

Work place: School of Computing and Creative Media, University of Technology Sarawak, 96000 Sibu, Sarawak, Malaysia

E-mail: khairunnisa.ibrahim@uts.edu.my

Website: https://orcid.org/0009-0004-2632-7164

Research Interests:

Biography

Khairunnisa Ibrahim holds a Master Degree in Computer Science, and a Bachelor Degree in Computer Science from University Malaysia Sarawak. Her research interest is in graph theory, machine learning, network visualization, and informational system.

Author Articles
Monkeypox Detection Using Support Vector Machine with a Quadratic Polynomial Kernel

By Michael Chi Seng Tang Siew Ping Yiiong Kee Chuong Ting Sing Ling Ong Marcella Peter Khairunnisa Ibrahim

DOI: https://doi.org/10.5815/ijem.2025.05.05, Pub. Date: 8 Oct. 2025

This study looks at how well a Support Vector Machine (SVM) with a quadratic polynomial kernel works for detecting Monkeypox. The SVM method is compared to other machine learning models like Neural Networks, KNN, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes. By using features from medical images called Local Binary Patterns (LBP), the SVM model showed the best results, with 93.33% accuracy, 95.24% recall, 91.67% true negative rate, and 90.91% precision. The LBP features are used because they exhibit unique textural patterns that can distinguish Monkeypox and normal cases. The results show that the SVM with this kernel is good at telling the difference between Monkeypox and normal cases, making it a helpful tool for early detection in healthcare.

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