Subject(s)
Carotid Artery, Internal/surgery , Carotid Stenosis/surgery , Cerebral Revascularization/methods , Emergencies , Endarterectomy, Carotid/methods , Ischemia/diagnosis , Ischemia/surgery , Retinal Diseases/diagnosis , Retinal Diseases/surgery , Aged , Carotid Stenosis/diagnosis , Early Medical Intervention , Fluorescein Angiography , Humans , Male , Postoperative Complications/diagnosis , Postoperative Complications/surgery , Recurrence , Reoperation , Tomography, Optical Coherence , Trabeculectomy , Treatment OutcomeABSTRACT
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-α) 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.
Subject(s)
Algorithms , Diabetes Mellitus/classification , Diabetes Mellitus/diagnosis , Diagnosis, Computer-Assisted , Artificial Intelligence , Databases, Factual , Fuzzy Logic , Humans , Software DesignABSTRACT
Diabetes is a type of disease in which the body fails to regulate the amount of glucose necessary for the body. It does not allow the body to produce or properly use insulin. Diabetes has widespread fallout, with a large people affected by it in world. In this paper; we demonstrate that a fuzzy c-means-neuro-fuzzy rule-based classifier of diabetes disease with an acceptable interpretability is obtained. The accuracy of the classifier is measured by the number of correctly recognized diabetes record while its complexity is measured by the number of fuzzy rules extracted. Experimental results show that the proposed fuzzy classifier can achieve a good tradeoff between the accuracy and interpretability. Also the basic structure of the fuzzy rules which were automatically extracted from the UCI Machine learning database shows strong similarities to the rules applied by human experts. Results are compared to other approaches in the literature. The proposed approach gives more compact, interpretable and accurate classifier.
