Investigating the Management of Datasets Featuring Elevated Dimensionality and a Restricted Patient Sample

The swift evolution of information technologies in today’s world has resulted in a significant increase in both the amount and variety of data in diverse fields. This dynamic context necessitates the implementation of efficient methods for data analysis to ensure quality processing and interpretation of vast amounts of information. This research focuses on addressing this pressing issue by developing an information system capable of handling extensive and diverse datasets. The primary objective is to create a toolkit that enables rapid and effective processing and analysis of information while ensuring high accuracy of results. Key aspects of our investigation include selecting optimal methods for data analysis, including feature selection and dimensionality reduction techniques aimed at identifying critical aspects of information and minimizing unnecessary complexity. Additionally, we explore opportunities for integrating these methods to achieve synergy and enhance overall data processing efficiency. This work holds significant importance for the advancement of information technologies and the provision of more precise and productive data analysis across various domains. The findings of our research can be utilized in practical applications, contributing to the development of intelligent systems and improving decision-making effectiveness.