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

Monkeypox Detection Using Support Vector Machine with a Quadratic Polynomial Kernel

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Author(s)

Michael Chi Seng Tang ^1,* Siew Ping Yiiong ¹ Kee Chuong Ting ² Sing Ling Ong ¹ Marcella Peter ¹ Khairunnisa Ibrahim ¹

1. School of Computing and Creative Media, University of Technology Sarawak, 96000 Sibu, Sarawak, Malaysia

2. School of Engineering and Technology, University of Technology Sarawak, 96000 Sibu, Sarawak, Malaysia

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2025.05.05

Received: 3 Dec. 2024 / Revised: 19 Jan. 2025 / Accepted: 8 May 2025 / Published: 8 Oct. 2025

Index Terms

Quadratic Polynomial Kernel, Support Vector Machine, Machine Learning, Local Binary Pattern Features, Medical Imaging

Abstract

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.

Cite This Paper

Michael Chi Seng Tang, Siew Ping Yiiong, Kee Chuong Ting, Sing Ling Ong, Marcella Peter, Khairunnisa Ibrahim, "Monkeypox Detection Using Support Vector Machine with a Quadratic Polynomial Kernel", International Journal of Engineering and Manufacturing (IJEM), Vol.15, No.5, pp. 58-65, 2025. DOI:10.5815/ijem.2025.05.05

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International Journal of Engineering and Manufacturing (IJEM)