HIERARCHICAL TAKAGI-SUGENO TYPE FUZZY SYSTEM FOR DIABETES MELLITUS FORECASTING
In this study, a new group method of data handling (GMDH) method, based on adaptive neurofuzzy inference system (ANF1S) structure, called ANFIS-GMDH and its application for diabetes mellitus forecasting is presented. Conventional neurofuzzy GMDH (NF-GMDH) uses radial basis network (RBF) as the partial descriptions. In this study the RBF partial descriptions are replaced with two input ANFIS structures and backpropagation algorithm is chosen for learning this network structure. The Prima Indians diabetes data set is used as training and testing sets which consist of 768 data whereby 268 of them are diagnosed with diabetes. The result of this study will provide solutions to the medical staff in determining whether someone is the diabetes sufferer or not which is much easier rather than currently doing a blood test. The results show that the proposed method performs better than the other models such as multi layer perceptron (MLP), RBF and ANFIS structure.
Hierarchical Takagi-Sugeno fuzzy system ANFIS structure Backpropagation algorithm Diabetic mellitus
ARASH SHARIFI ASIYEH VOSOLIPOUR MAHDI ALIYARI SH MOHAMMAD TESHNEHLAB
Computer department of Islamic Azad University Science and Research Branch, Tehran, Iran Electrical Engineering department of K.N.Toosi University of Technology, Tehran, Iran
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
1265-1270
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）