%0 Journal Article %1 noauthororeditor %A Sa’di, Sadri %A Hashemi, Ramin %A Abdollapour, Arman %A Chalabi, Kamal %A Salamat, Mohammad Amin %D 2015 %J International Journal on Foundations of Computer Science & Technology (IJFCST) %K (ANNs) (PNN) Artificial Network Networks Neural Probabilistic accuracy disease heart test training %N 6 %P 7 %R 10.5121/ijfcst.2015.5605 %T A NOVEL PROBABILISTIC ARTIFICIAL NEURAL NETWORKS APPROACH FOR DIAGNOSING HEART DISEASE %U https://wireilla.com/papers/ijfcst/V5N6/5615ijfcst05.pdf %V 5 %X In today's world one of the most common diseases are heart disease which its mortality and disability is high. Therefore, heart disease is one of the biggest health problems in the world. Since the diagnosis of heart disease in people is very important, a method should be used in the right diagnosis of heart diseases that have the least errors in heart disease diagnosis. For this reason, in this paper, Probabilistic Neural Networks (PNNs) for the diagnosis of heart disease from a dataset that includes 303 samples from different patients is used. In this paper, we have implemented PNN in the MATLAB environment. As well as, the efficiency criteria in this paper is to maximize accuracy of heart disease diagnosis in the process of training and testing. According to the Cleveland dataset which contains 303 samples, we found that the accuracy of training and test accuracy are 87% and 75% respectively.

@article{noauthororeditor, abstract = {In today's world one of the most common diseases are heart disease which its mortality and disability is high. Therefore, heart disease is one of the biggest health problems in the world. Since the diagnosis of heart disease in people is very important, a method should be used in the right diagnosis of heart diseases that have the least errors in heart disease diagnosis. For this reason, in this paper, Probabilistic Neural Networks (PNNs) for the diagnosis of heart disease from a dataset that includes 303 samples from different patients is used. In this paper, we have implemented PNN in the MATLAB environment. As well as, the efficiency criteria in this paper is to maximize accuracy of heart disease diagnosis in the process of training and testing. According to the Cleveland dataset which contains 303 samples, we found that the accuracy of training and test accuracy are 87% and 75% respectively. }, added-at = {2023-05-17T14:34:09.000+0200}, author = {Sa’di, Sadri and Hashemi, Ramin and Abdollapour, Arman and Chalabi, Kamal and Salamat, Mohammad Amin}, biburl = {https://www.bibsonomy.org/bibtex/2dafd68e4b395770650aa73261bf73633/devino}, doi = {10.5121/ijfcst.2015.5605}, interhash = {c7cce22c963733603a1ddcc1fbe15362}, intrahash = {dafd68e4b395770650aa73261bf73633}, issn = {1839-7662}, journal = {International Journal on Foundations of Computer Science & Technology (IJFCST)}, keywords = {(ANNs) (PNN) Artificial Network Networks Neural Probabilistic accuracy disease heart test training}, language = {ENGLISH}, month = nov, number = 6, pages = 7, timestamp = {2023-05-17T14:34:09.000+0200}, title = {A NOVEL PROBABILISTIC ARTIFICIAL NEURAL NETWORKS APPROACH FOR DIAGNOSING HEART DISEASE}, url = {https://wireilla.com/papers/ijfcst/V5N6/5615ijfcst05.pdf}, volume = 5, year = 2015 }