文章基本信息

标题：A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II
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作者：Zahed Soltani ; Ahmad Jafarian
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：6
DOI：10.14569/IJACSA.2016.070611
出版社：Science and Information Society (SAI)
摘要：Diabetes is one of the major health problems as it causes physical disability and even death in people. Therefore, to diagnose this dangerous disease better, methods with minimum error rate must be used. Different models of artificial neural networks have the capability to diagnose this disease with minimum error. Hence, in this paper we have used probabilistic artificial neural networks for an approach to diagnose diabetes disease type II. We took advantage of Pima Indians Diabetes dataset with 768 samples in our experiments. According to this dataset, PNN is implemented in MATLAB. Furthermore, maximizing accuracy of diagnosing the Diabetes disease type II in training and testing the Pima Indians Diabetes dataset is the performance measure in this paper. Finally, we concluded that training accuracy and testing accuracy of the proposed method is 89.56% and 81.49%, respectively
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; diabetes type 2; probabilistic artificial neural networks; data mining; mean squares error; Naive Bayes