Meta Aprendizagem de Extração de Caracterı́sticas Aplicada ao Diagnóstico de Glaucoma

Glaucoma is an asymptomatic disease that can bring people to blind- ness if not early detected. Computational intelligence methods have been pro- posed to provide a computerized diagnosis that can guide patients to the ap- propriate treatment. However, these techniques face methodology optmization problems, which depends on the choices of many algorithms from diferent kno- wledge areas. This paper suggests a solution through meta-learning of pre- processing methods, decomposition and features extraction which have to be used efficiently in order to solve the problem. Current results are promissing, reaching 91.24% accuracy after 50 evaluations and it is suposed to improve proportionally to the number of evaluations.