Early Detection of Glaucoma Using Fuzzy Logic in Bangladesh Context

Detecting Glaucoma at an early stage is very crucial to prevent irreversible vision loss. Glaucoma detection often requires advanced diagnosis which are not accessible to most of the hospitals in a developing country like Bangladesh. Therefore, detection of Glaucoma with the help of common and fewer tests would certainly improve the condition of the Glaucoma patients. In this paper, a method is devised to detect Glaucoma with the data obtained from two ophthalmological tests which are OCT and tonometry along with some other risk factors. The method uses Adaptive Neuro-Fuzzy Inference System (ANFIS) to train an Artificial Intelligence model which can make prediction about the presence of Glaucoma, the absence of it and whether the patient is suspected to have Glaucoma. Conventional Glaucoma detection techniques suggest the use of Intraocular pressure (IOP) as one of the primary parameters to detect Glaucoma. But by analyzing data of Glaucoma patients of Bangladesh, we observed that IOP is not the only significant factor for detecting Glaucoma. Our proposed method gives an accuracy of 81.25% using the ANFIS model.