Noise-Aware Undersampling for imbalanced medical data (NAUS)

Buribayev Z. Yerkos A. Zhetpisbay Z. Wolfien M.
January 2026 Elsevier Ltd

Informatics in Medicine Unlocked
2026 #60

Advancements in medical research have increasingly relied on robust data analytics to support diagnostic and treatment decisions. However, data analysis still faces challenges when investigating datasets with severe class imbalance, often stemming from the rarity of certain conditions and uneven disease distributions. To address this issue, we propose the Noise-Aware Undersampling with Subsampling (NAUS) algorithm. NAUS integrates clustering, noise removal, and Tomek-link identification techniques to create refined subsamples that assess the significance of individual observations, while systematically removing redundant and noisy data. The proposed approach was evaluated on datasets related to chronic kidney disease, liver disease, heart disease and its performance was compared to that of traditional oversampling methods (e.g., SMOTE, ADASYN, LoRAS) and undersampling techniques (e.g., random undersampling, Tomek-links). Our experimental results, based on machine learning classifiers (e.g. Random Forest, LightGBM, and Multilayer Perceptron). Data visualization further confirmed that NAUS effectively mitigates class imbalance, making it a promising tool for enhancing the reliability of medical data analysis.

Data analysis , Data balancing , Noise removal , Tomek-link , Undersampling

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Department of Computer Science, Al-Farabi Kazakh National University, al-Farabi Avenue, Almaty, 050040, Kazakhstan
TUD Dresden University of Technology, Faculty of Medicine Carl Gustav Carus, Institute for Medical Informatics and Biometry, Fetscherstr. 74, Dresden, 01307, Germany
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Strehlener Str. 12-14, Dresden, 01069, Germany

Department of Computer Science
TUD Dresden University of Technology
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)

10 лет помогаем публиковать статьи Международный издатель

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