Enhanced Prediction of Heart Disease Using Particle Swarm Optimization and Rough Sets with Transductive Support Vector Machines Classifier

Over the last decade heart disease has significantly increased and it has
emerged to be the primary reason behind the mortality in people living in many
nations across the world. The computer-assisted systems act as a tool for the doctors
in the prediction and diagnosis of heart disease. In the medical domain, Data Mining
yields a variety of techniques that are extensively employed in the medical and clinical
decision support systems that has to be quite useful in diagnosing and predicting the
heart diseases with less time and good accuracy to improve their health. The previous
system designed a radial basis function with support vector machine for heart disease
prediction. However it does not provides a satisfactory classification result. To solve
this problem the proposed system designed a Particle Swarm Optimization and RoughSets with Transductive Support Vector Machines (PSO and RS with TSVM) based
prediction is performed. In this proposed work, the dataset of the heart disease iscollected from UCI repository. In order to reduce data redundancy and improve data
integrity, the data normalization is performed by using Zero-Score (Z-Score). Then