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.

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