# Artificial Intelligence-Based System for Retinal Disease Diagnosis
## 5 Conclusions  
Today the problem of ophthalmological diseases, in particular retinal diseases, remains relevant and significant. New solutions for the diagnosis of retinal pathologies are required; it would detect the disease at an early stage and increase the diagnosis efficiency and accuracy. This opens up new prospects for the development of innovative approaches to the treatment of retinal pathologies and contributes to a significant reduction in the incidence of blindness and disability due to eye diseases.  
To increase the accuracy and speed of retinal pathology diagnosis, the paper proposes an approach for diagnosing retinal pathologies based on an electrophysiological complex and AI decision support system. A decision support system based on artificial intelligence methods can not only improve the quality of medical diagnostics, but also predict the disease courses. This system facilitates data analysis, accurate diagnoses and the development of effective treatment recommendations. It makes it easier for doctors to make informed  
Figure 9: Color distribution scheme of retinal pathology marker factors.  
decisions based on clinical data and scientific research, which ultimately leads to higher treatment efficiency.  
In this study, we have presented a thorough systems analysis of existing methods, models and technologies for diagnosing eye diseases. An intelligent system has been developed that includes an electrophysiological complex for hardware diagnostics and an intelligent decision support system based on a fuzzy model. A functional diagram of the operation of the electrophysiological complex as part of an intelligent system has been developed, which has defined an algorithm for diagnosing retinal disease for a doctor.  
A system of key factors has been constructed that directly or indirectly affects the occurrence of retinal pathologies. These are three groups of factors--measurement data, anamnesis data and socio demographic data. These factors formed the basis of a fuzzy model and the formation of a rule base. The fuzzy model as a component of an AI decision support system provides support for decision making on diagnostics and differs from the existing ones by taking into account both objective factors--data obtained from the electrophysiological complex and socio demographic factors about the patient--and subjective factors--patient history data (anamnesis).  
The problem of retinal disease diagnostics based on a set of heterogeneous data is considered as a multi-class classification on unbalanced data. The decision support system includes two classifiers--one classifier based on a fuzzy model and a fuzzy rule base (RB-classifier) and one using the stochastic gradient boosting algorithm (SGB-classifier). The efficiency of the algorithms in multi-class classification on unbalanced data is estimated based on two indicators--MAUC and MMCC. Combining these two algorithms in the decision support system provides more accurate and reliable pathology identification.  
A series of scenario experiments with various combinations of input factors have been conducted; the results of statistical analysis identify the main determinants (markers) for each diagnosis. The developed model as part of the electrophysiological complex, which provides the doctor with decision support in retinal diseases diagnosis, has practical significance. The proposed approach is universal and can be used not only in ophthalmology, but also in neurology, cardiology and other areas where diagnostic methods are present that record tissue bioelectric activity.  
This research received no external funding.