Use of data Mining in Identifying the risk factors of optic neuropathy

Abstract The optic nerve carries signals from the eye to the brain, where they are interpreted as visual images. Optic neuropathy is a serious eye disease that can lead to the loss of vision in the affected eye. Identifying the risk factors for optic neuropathy from large patient data is crucial and challenging. Modern techniques can assist in recognizing these risk factors. For instance, data mining algorithms such as classification and association rules can discover knowledge from datasets in many real-world applications, particularly in the medical field. This study employed a decision tree algorithm known as J48 and an association rule algorithm called Apriori to analyze the collected data. The J48 algorithm achieved an accuracy of 90%, while the Apriori algorithm discovered 52 significant association rules with a confidence level above 80%. The goal of this study was to identify risk factors for optic neuropathy and explore the connection between optic nerve damage and other conditions. The proposed algorithms aim to reduce blindness rates and increase awareness of the risk factors associated with optic nerve damage by detecting hidden risk factors at an early stage. The study's findings show that some risk factors for optic neuropathy confirmed by medical trials are also detected by these algorithms, proving the effectiveness and applicability of data mining techniques in the medical field. Moreover, this study discovered new risk factors for optic nerve damage not previously found by medical trials. This knowledge will contribute to the early detection and prevention of blindness by recognizing risk factors for optic nerve damage.