Application of fuzzy sets to the diagnosis of glaucoma

Glaucoma is one of the leading causes of blindness in the world. There are several methods of treatment which can impede glaucoma´s progression, but there is no way to reverse the damage already done. For this reason, if is important to diagnose glaucoma as early as possible to minimize the damage to the optic nerve. While there are many different tests used to detect the damage caused by glaucoma, there is no one test that looks at all of the possible effects. A methodology for using fuzzy set theory to combine these results into one diagnosis was developed. The methodology included two variations of three different techniques resulting in six different algorithms. The algorithms were verified with a set of patient data and the accompanying physician´s diagnosis. The results of the algorithms were compared to determine the ones with a higher degree of correlation to the physician´s diagnosis