Оцінювання біологічного віку з використанням мереж Колмогорова–Арнольда на малих даних

• Datum: 2026
• Hauptverfasser: Slipchenko, Volodymyr, Poliahushko, Liubov, Rudyk, Volodymyr, Shatylo, Vladyslav
• Format: Artikel
• Sprache: Englisch
• Veröffentlicht: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2026
• Schlagworte: мережі Колмогорова–Арнольда; біологічний вік; мінеральна щільність кісткової тканини; машинне навчання; глибинне навчання
• Online Zugang: https://journal.iasa.kpi.ua/article/view/365272
• Назва журналу: System research and information technologies

This article explores the issue of the application of Kolmogorov–Arnold Networks (KAN) for biological age estimation using a dataset of 344 male patients. The dataset includes biomarkers related to bone health and body composition. To enhance model performance, data preprocessing techniques such as polynomial interpolation for missing values and standardization were applied. Pearson and Spearman correlation analyses identified the most relevant biomarkers. Machine learning models were evaluated, along with neural networks and KANs. Experimental results demonstrate that KANs outperform traditional machine learning models and classical neural networks on small datasets. The optimal KAN architecture achieved a correlation coefficient of 0.93, a mean squared error of 18.81, and a mean absolute error of 2.8, surpassing the best-performing conventional models. These findings highlight the potential of KANs as a robust alternative for biological age estimation in resource-limited settings.