TY  - JOUR
T1  - A Hybrid Classification Model for Multivariate Heart Disease Dataset Using Enhanced Support Vector Machine Technique
AU - AnandhaKumar, P. AU - NaliniPriya, G. AU - Kannan, A. 
JO  - International Journal of Soft Computing
VL  - 7
IS  - 5
SP  - 242
EP  - 248
PY  - 2012
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2012.242.248
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2012.242.248
KW  - CAD
KW  -heart disease
KW  -classification
KW  -multivariate data
KW  -BPN
KW  -SVM
KW  -ANN
KW  -random values
AB  - In Medical Information Systems, the data available for the 
  learning and prediction are multivariate in nature. Some of the classification 
  models which were generally used in the design of medical decision support systems 
  could not provide a good performance. In this study, researchers address the 
  ways to improve the performance of a supervised learning based classification 
  algorithm. For achieving this, researchers propose the use of statistical technique 
  for performing effective decision making in medical application, screening and 
  manipulating the training samples with little bit of Gaussian Distribution Random 
  Values (GDRV) before using the data for training the neural network. This study 
  present, a way to improve the performance of a neural network based classification 
  model through the proposed biased training algorithm which has been evaluated 
  with the Coronary Artery Disease (CAD) data sets taken from University California 
  Irvine (UCI). The performance has been evaluated with standard metrics.
ER  -