Comput Methods Programs Biomed - Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm.

Tópicos

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Resumo

In this study, diagnosis of diabetes disease, which is one of the most important diseases, is conducted with artificial intelligence techniques. We have proposed a novel Artificial Bee Colony (ABC) algorithm in which a mutation operator is added to an Artificial Bee Colony for improving its performance. When the current best solution cannot be updated, a blended crossover operator (BLX-a) of genetic algorithm is applied, in order to enhance the diversity of ABC, without compromising with the solution quality. This modified version of ABC is used as a new tool to create and optimize automatically the membership functions and rules base directly from data. We take the diabetes dataset used in our work from the UCI machine learning repository. The performances of the proposed method are evaluated through classification rate, sensitivity and specificity values using 10-fold cross-validation method. The obtained classification rate of our method is 84.21% and it is very promising when compared with the previous research in the literature for the same problem.

Resumo Limpo

studi diagnosi diabet diseas one import diseas conduct artifici intellig techniqu propos novel artifici bee coloni abc algorithm mutat oper ad artifici bee coloni improv perform current best solut updat blend crossov oper blxa genet algorithm appli order enhanc divers abc without compromis solut qualiti modifi version abc use new tool creat optim automat membership function rule base direct data take diabet dataset use work uci machin learn repositori perform propos method evalu classif rate sensit specif valu use fold crossvalid method obtain classif rate method promis compar previous research literatur problem

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