Weighted SMOTE Algorithm: A Tool To Improve Disease Prediction With Imbalanced Data

In the medical field, acquiring a sufficient number of medical samples can be challenging, and the collected datasets may be imbalanced and small. To address these issues, we propose a weighted SMOTE algorithm that targets imbalanced datasets. This technique has been applied to a dataset of breath biomarkers of liver disease as a feature set and a supervised learning model. Our results show that the proposed method significantly improves the prediction probability and classification performance of the chosen model in both the original imbalanced dataset and the balanced dataset. This study demonstrates the potential of the proposed approach to enhance machine learning performance while dealing with small and imbalanced datasets in medical applications.