Today, the field of biomedical engineering spans numerous areas of scientific research that
grapple with the challenges of intelligent analysis of small datasets. Analyzing such datasets …

The problem of predicting the success of organ transplantation is critical in the field of
medicine. The use of a probabilistic neural network is of considerable interest in this context …

INTRODUCTION: The task of approximation of complex nonlinear dependencies, especially
in the case of short datasets, is important in various applied fields of medicine. Global …

In the era of big data processing, numerous techniques prove valuable for analyzing large-
scale datasets. However, the efficient processing of small data is equally crucial, particularly …

Improving the accuracy of intelligent data analysis is an important task in various application
areas. Existing machine learning methods do not always provide a sufficient level of …

The accuracy of solving the approximation tasks in the case of analysis of large volumes of
tabular biomedical datasets by machine learning methods is not always high. This is …

Clinical data are increasingly being used to generate new medical knowledge to improve
diagnostic accuracy, better personalize therapeutic regimens, improve clinical outcomes …

Amidst rapid technological and healthcare advancements, biomedical data classification
using machine learning (ML) is pivotal for revolutionizing medical diagnosis, treatment, and …

Intellectual analysis of small and middle-sized datasets through machine learning tools
presents challenges in various application domains. Existing methods fail to provide …

Background: Renal transplantation is a life-saving procedure and contributes to a better
quality of life in patients with end-stage renal disease. The discovery and use of …