Begic Fazlic Lejla, Avdagic Aja, Besic Ingmar
Faculty of Electrical Engineering, University of Sarajevo, Bosnia and Herzegovina.
Faculty of Medicine, Ludwig Maximilian University of Munich, Germany.
Stud Health Technol Inform. 2015;211:292-4.
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.
本研究的目的是开发一种新颖的遗传算法-自适应神经模糊推理系统（GA-ANFIS）专家系统原型，通过使用心脏病属性（特征）和实际病例诊断来对患者的心脏病程度进行分类。13个属性被用作基于自适应神经模糊推理系统（ANFIS）的分类器的输入，用于模糊模型优化的第一级。它们在遗传算法（GA）中用作输入，用于GA-ANFIS系统中模糊模型优化的第二级。GA-ANFIS系统分两步进行优化。在MATLAB环境中对新型GA-ANFIS系统方法进行建模和验证。我们比较了GA-ANFIS和ANFIS的结果。就心脏病诊断而言，所提出的带有预测值技术的GA-ANFIS模型比我们通过ANFIS模型得到的早期方法更有效。
