High-Performance Classification of Mpox Symptoms Using Support Vector Classifier and Quadratic Discriminant Analysis

These findings demonstrate the strong potential of machine learning classifiers for detecting Mpox based on clinical features. Incorporating these models into healthcare systems could significantly enhance early case detection, improve clinical decision-making, and bolster disease surveillance. Future research should focus on prospective validation of these ML classifiers in real-world clinical environments.

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Publication Details

Journal
medRxiv
Published
2026-02-22
DOI
https://doi.org/10.64898/2026.02.12.26346046
Primary Topic
Poxvirus research and outbreaks
Type
article
Field-Weighted Citation Impact
0.00

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article

High-Performance Classification of Mpox Symptoms Using Support Vector Classifier and Quadratic Discriminant Analysis

Kolapo Oyebola, Michael Olufemi, Funmilayo C. Ligali, Shalom C Okoli
medRxiv
Poxvirus research and outbreaks
article

High-Performance Classification of Mpox Symptoms Using Support Vector Classifier and Quadratic Discriminant Analysis

Kolapo Oyebola, Michael Olufemi, Funmilayo C. Ligali, Shalom C Okoli
article en

Abstract

These findings demonstrate the strong potential of machine learning classifiers for detecting Mpox based on clinical features. Incorporating these models into healthcare systems could significantly enhance early case detection, improve clinical decision-making, and bolster disease surveillance. Future research should focus on prospective validation of these ML classifiers in real-world clinical environments.

medRxiv
Mountain Top University (NG), Nigerian Institute of Medical Research (NG)
Bill and Melinda Gates Foundation, African Academy of Sciences, National Institutes of Health, Fogarty International Center
Reduced inequalities
Openalex Percentile: Top 49%
Poxvirus research and outbreaks
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