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Original Research Article

Comparative Performance Analysis of Synthetic Minority Oversampling Techniques (SMOTE) on Medical Datasets Based on Extreme Gradient Boosting Estimator

Yusuf, A. K, Hassan, Adam, Yusuf A

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Yusuf, A. K Corresponding Author

Centre for Enterprenureship and Enterprise Development, University of Maiduguri, Nigeria

Correspondence: yusufkutigi1970@gmail.com

Received
30 Jun 2026
Published
14 Jul 2026

Abstract

Medical datasets frequently face issues with class imbalance and redundant features, which can undermine the accuracy and reliability of predictive diagnostic models. This research conducts a performance comparison of four variants of the Synthetic Minority Oversampling Technique (SMOTE)—SMOTE-ENN, Borderline SMOTE, ADASYN, and SMOTE-Tomek Links—when paired with feature selection and the Extreme Gradient Boosting (XGBoost) classifier for predicting breast cancer and heart disease. The publicly available datasets underwent preprocessing to eliminate noise, balance class distributions, and identify the most significant diagnostic features. Model performance was assessed using standard metrics, including accuracy, precision, recall, F1-score, and Cohen’s kappa. For the heart disease dataset, the SMOTE-ENN technique produced the highest results, achieving an accuracy of 56.15%, a recall of 44.50%, and an F1-score of 27.46%, which underscored improved detection of minority class cases. Conversely, in the breast cancer dataset, ADASYN, Borderline SMOTE, and SMOTE-Tomek Links showed better performance, reaching an accuracy of 96.49%, an F1-score of 97.30%, a recall of 100%, and a kappa score of 0.9231.

Keywords: Smote, Feature Selection, Xgboost, Class Imbalance, Breast Cancer, Heart Disease, Machine Learning, Medical Diagnosis.

How to Cite

APA

Yusuf, A. K., Hassan, A., & A, Y. (2026). Comparative Performance Analysis of Synthetic Minority Oversampling Techniques (SMOTE) on Medical Datasets Based on Extreme Gradient Boosting Estimator. Journal of Contemporary Academic Research and Methodologies, 1(5). https://doi.org/10.5281/zenodo.21353004

MLA

Yusuf, A. K, Adam Hassan, and Yusuf A. "Comparative Performance Analysis of Synthetic Minority Oversampling Techniques (SMOTE) on Medical Datasets Based on Extreme Gradient Boosting Estimator." Journal of Contemporary Academic Research and Methodologies, vol. 1, no. 5, 2026. DOI: https://doi.org/10.5281/zenodo.21353004

Chicago

Yusuf, A. K, Adam Hassan, and Yusuf A. "Comparative Performance Analysis of Synthetic Minority Oversampling Techniques (SMOTE) on Medical Datasets Based on Extreme Gradient Boosting Estimator." Journal of Contemporary Academic Research and Methodologies 1, no. 5 (2026). https://doi.org/10.5281/zenodo.21353004

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ISSN 3139-7247
Tracking ID JCARM_JUN_26_168
Article No. 040
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Article Info
Journal JCARM
Volume Vol 1, No 5
Year 2026
Type Original Research Article
Licence CC BY-NC-SA 4.0
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