THE ALGORITHM AND SOFTWARE TOOL A COMPLEX OF INFORMATIVE SIGNS IN THE CLASSIFICATION OF DISEASES OF THE CIRCULATORY SYSTEM

Abstract

Background: Accurate and efficient classification of diseases of the
circulatory system is crucial for patient management. Identifying informative signs
and their relationships can enhance disease classification.
Objective: To develop an algorithm and software tool for choosing a complex
of informative signs in the classification of diseases of the circulatory system.
Methods: A dataset of patient records was used to extract clinical signs and apply
feature selection and clustering techniques. Candidate complexes were formed and
evaluated using machine learning classifiers. The algorithm was implemented in a
software tool called CIRCULATORY-SIGN.
Results: The algorithm identified a complex of informative signs that
significantly improved disease classification accuracy compared to single signs.
CIRCULATORY-SIGN automated the process, providing a user-friendly interface
and customizable options.
Conclusion: The algorithm and software tool provide a valuable approach for
selecting informative signs and improving the classification of circulatory system
diseases.