title: Prediction of Heart Attack Risk Using GA-ANFIS Expert System Prototype.
abstract: The aim of this research is to develop a novel GA-ANFIS expert system prototype for classifying heart disease degree of a patient by using heart diseases attributes (features) and diagnoses taken in the real conditions. Thirteen attributes have been used as inputs to classifiers being based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. They are used as inputs in Genetic Algorithm (GA) for the second level of fuzzy model optimization within GA-ANFIS system. GA-ANFIS system performs optimization in two steps. Modelling and validating of the novel GA-ANFIS system approach is performed in MATLAB environment. We compared GA-ANFIS and ANFIS results. The proposed GA-ANFIS model with the predicted value technique is more efficient when diagnosis of heart disease is concerned, as well the earlier method we got by ANFIS model.
authors.affiliation: Faculty of Electrical Engineering, University of Sarajevo, Bosnia and Herzegovina.
authors.forename: Lejla
authors.initials: L
authors.lastname: Begic Fazlic
authors.affiliation: Faculty of Medicine, Ludwig Maximilian University of Munich, Germany.
authors.forename: Aja
authors.initials: A
authors.lastname: Avdagic
authors.affiliation: Faculty of Electrical Engineering, University of Sarajevo, Bosnia and Herzegovina.
authors.forename: Ingmar
authors.initials: I
authors.lastname: Besic
mesh_terms: D000465:Algorithms; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D000069550:Machine Learning; D009203:Myocardial Infarction / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D013673:Technology Assessment, Biomedical