HEART DISEASE DETECTION USING RADIAL BASIS FUNCTION CLASSIFIER
Abstract
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A Radial Basis Classification method for classifying heart disease from clinical databases has been introduced in this article. Multivariate attribute classifiers may have many parameters and are difficult to differentiate between their ideal attributes. The Multivariate Function Classifier Ideas will encourage the more consolidated stochastic trends to minimise the possibility of making mistakes or new secret results. This formula is useful to order the multidimensional data while optimising the grouping accuracy in the restore analysis. The results obtained from this work reveal, thus, that the calculation suggested provides higher precision than previous strategies.

Authors
A Saravana Kumar
Ponnaiyah Ramajayam Institute of Science and Technology, India

Keywords
Neural Network, RBF, Prediction, Heart Disease
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Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 1 , Issue: 4 , Pages: 105-108 )
Date of Publication :
September 2020
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598
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