Artificial intelligence Integration in the diagnosis, prognosis and diabetic neovascular glaucoma treatment

Abstract

This work is focused on key aspects of the diagnosis, prognosis and treatment of neovascular glaucoma of diabetic origin based on machine learning approaches and, in particular, various architectures artificial neural models. An analysis of the relevance, priority provisions and advantages of using machine learning methods is carried out, the existing approaches used in modern literature in the context of the topic under study are considered, the specifics of their integration into the process of diagnostic analysis of the feature space of an aggregated and labeled by the authors data set on patients with visual problems are described, in particular, those suffering from neovascular glaucoma of diabetic origin. A correlation analysis of input features was carried out, 3 different models of artificial neural networks were built, trained and tested, metrics for assessing the accuracy of their work were experimentally calculated and studied, and statistical indicators were determined, including errors and losses, characterizing their generalizing ability. Analysis of the results obtained from the studies made it possible to identify the prevailing input features and evaluate their impact on the target output variable and the overall significance in the feature space of the data set, as well as to establish the most suitable models for data analysis in terms of their accuracy and speed. The conducted research made it possible to establish the fact of a greater degree deep learning artificial neural networks models fully connected adaptability for the analyzed data set