International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Automated Monkeypox Classification Using ReliefF Feature Selection and Stacked Ensemble Learning with Optimized Performance Metrics

Abstract

The accurate and in time detection of Monkeypox using medical images is crucial for effective disease management. In this paper an improved classified system that integrates texture, local binary pattern (LBP), and statistical features with advanced feature selection and ensemble learning has been proposed.  ReliefF algorithm was used as feature selection, maintaining the top 70%of features, and hyperparameter optimization has been applied to the Support Vector Machine (SVM) classifier. Additional algorithms: Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, and a Stacked Ensemble model were also used as classifier. Different metrics like accuracy, precision, recall, and F1-score. The highest accuracy of 92.5%. Confusion matrix and the area under the Receiver Operating Characteristic (ROC) curve (AUC) visually demonstrate model performance. The overall results confirmed that integrating feature selection with ensemble learning can significantly improve the robustness and reliability of automated Monkeypox detection.

Country : Iraq

1 Wameedh Raad Fathel

  1. Ministry of Education, General Directorate of Education in Nineveh, Iraq

IRJIET, Volume 9, Issue 11, November 2025 pp. 175-186

doi.org/10.47001/IRJIET/2025.911020

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