References

JCARM

Journal of Contemporary Academic Research and Methodologies

JCARM

3139-7247

Ivory and Finch Publishers

10.5281/zenodo.21353004

JCARM_JUN_26_168

Original Research Article

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

A. K

Yusuf,

Centre for Enterprenureship and Enterprise Development, University of Maiduguri, Nigeria yusufkutigi1970@gmail.com Adam

Hassan,

Department of Software Engineering, Al-Ansar University, Maiduguri, Nigeria A

Yusuf

Department of Computer Engineering, University of Maiduguri, Nigeria.

14 07 2026

1 5

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.

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

30 06 2026 03 07 2026

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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