An Early Detection Method for Age-Related Macular Degeneration Using a Probabilistic Inference Algorithm

Abstract

An age-related retinal disease known as age-related macular degeneration (ARMD) is the leading cause of blindness in the elderly. Age-related macular degeneration (ARMD) is a major global cause of blindness. Although there is currently no treatment for ARMD, the disease can be managed with early detection through a balanced lifestyle and frequent exercise. The importance of early ARMD detection in limiting the disease's progression is growing in tandem with the proportion of the population that is 65 and older. This study aims to analyze huge datasets gathered from past instances, which contain eye pictures and aspects of risk factors that might cause ARMD, in order to design an algorithm based on probabilistic inference using Bayesian Network. This work combines analysis of eye pictures with investigation of several causes causing ARMD, which is different from other techniques in diagnosing the condition. Factors that induce ARMD should be included in studies and analyses since they serve as effective markers for when early diagnosis is needed.