TY  - JOUR
T1  - Efficient Heart Disease Prediction with Artificial Neural Network,
Radial Basis Function and Case Based Reasoning
AU - Kumar, R. Jothi AU - Sivabalan, R.V. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 24
SP  - 4995
EP  - 5003
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.4995.5003
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.4995.5003
KW  - Artificial Neural Network (ANN)
KW  -Case Based Reasoning (CBR)
KW  -Generalized Regression Neural Network (GRNN)
KW  -Radial Basis Function (RBF)
KW  -data base
AB  - Heart disease is one of the most hazardous diseases to human which shows the way to death all over the world since 15 year. Many researches have been done with the techniques of knowledge discovery in various fields for heart disease prediction and have shown the acceptable levels of accuracy. By investigating the survey of those accuracy levels, this research paper is proposed to help doctors not only to diagnose and predict the heart disease by achieving accuracy levels but also helps to prescribe the medicine successfully according to the predicted disease. In this paper assessment is done by two methodologies Artificial Neural Network (ANN) by testing the datasets, Case Based Reasoning (CBR ) image similarity search by mapping the similarities of images of old patients stored in database for prediction of heart disease. The result of the evaluation of CBR is also implemented for prescribing medicine from the history of old patients with generalized regression neural network and radial basis function successfully.
ER  -