HIERARCHICAL MACHINE LEARNING SYSTEM FOR FUNCTIONAL DIAGNOSIS OF EYE PATHOLOGIES BASED ON THE INFORMATIONEXTREMAL APPROACH

Abstract

Context. The task of information-extremal machine learning for the diagnosis of eye pathologies based on the characteristic signs of diseases is considered. The object of the study is the process of hierarchical machine learning in the system for diagnosing ophthalmological diseases. The aging population and the increasing prevalence of eye diseases, such as glaucoma,  optic nerve atrophy, retinal detachment, and diabetic retinopathy, necessitate effective methods for early diagnosis to prevent vision loss. Traditional diagnostic methods largely rely on the experience of the physician, which can lead to errors. The use of artificial intelligence (AI) and machine learning (ML) can significantly improve the accuracy and speed of diagnosis, making this topic highly relevant.
Objective. To enhance the functional efficiency of a computerized system for diagnosing eye pathologies based on image data.
Method. A method of information-extremal hierarchical machine learning for a system of eye pathology diagnosis based on the characteristic signs of diseases is proposed. The method is based on a functional approach to modeling cognitive processes of natural intelligence, ensuring the adaptability of the diagnostic system under any initial conditions for the formation of pathology images and allowing flexible retraining of the system when the recognition class alphabet expands. The foundation of the method is the principle of maximizing the criterion of functional efficiency based on a modified Kullback information measure, which is a functional of the diagnostic rule precision characteristics. The learning process is considered as an iterative procedure for optimizing the parameters of the diagnostic system’s operation according to this information criterion. Based on the proposed categorical functional model, an information-extremal machine learning algorithm with a hierarchical data structure in the form of a binary recursive tree is developed. This data structure enables the division of a large number of recognition classes into pairs of nearest neighbors, for which the machine learning parameters are optimized using a linear algorithm of the necessary depth.
Results. An intelligent technology for diagnosing eye pathologies has been developed, which includes a comprehensive set of information, algorithmic, and software components. A comparative analysis of the effectiveness of different methods for organizing decision rules during system training has been conducted. It was found that the use of recursive hierarchical classifier structures allows achieving higher diagnostic accuracy compared to binary classifiers.
Conclusions. The developed intelligent computer-based diagnostic system for eye pathologies demonstrates high efficiency and accuracy. The implementation of such a system in medical practice could significantly improve the quality of eye disease diagnostics, reduce the workload on physicians, and minimize the risk of misdiagnosis. Further research could focus on refining algorithms and expanding their application to other types of medical images