A Hybrid Classification Model for Multivariate Heart Disease Dataset Using Enhanced Support Vector Machine Technique

摘要

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.