Abstract: 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

Zahed Soltani and Ahmad Jafarian, “A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II” International Journal of Advanced Computer Science and Applications(IJACSA), 7(6), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070611

@article{Soltani2016,
title = {A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070611},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070611},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {6},
author = {Zahed Soltani and Ahmad Jafarian}
}