Laterality condition analysis on non-arteritic anterior ischemic optic neuropathy patient in one of the hospital in Jakarta with medical data mining

Abstract

Non-arteritic Anterior Ischemic Optic Neuropathy (NAION) is a disease caused by blood shortages in the artery that supplies the optic disc. Risk factors for NAION are hypertension, obesity, diabetes, dislipidemia, smoking, hypercoagulable state, cardiovascular disease, and stroke. NAION can result from unilateral or bilateral conditions. This study will focus on the identification of important factors that could distinguish characteristics between unilateral and bilateral patients. Random forest method is applied to obtain factors that can consistently distinguish characteristic between each laterality condition. Decision trees and the logistic regression method are added to obtain the visualization of the role of each important factors in the form of classification tree and the risk comparison of patients for experiencing a certain laterality condition by using odds ratios. The important factors based on random forest model are onset, fasting blood glucose levels, high density lipoprotein levels, age, two-hour postprandial glucose levels, and low density lipoprotein levels. Based on the odds ratio, advancing age and high density lipoprotein levels will decrease the risk of patients experiencing bilateral condition; on the other hand, the risk of bilateral condition will increase if other important factors are also increased.