Author(s):
1. SADIYAMOLE P A:
Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore 21, India
2. S MANJU PRIYA:
Department of CS, Karpagam Academy of Higher Education, Coimbatore 21, India
Abstract:
It is assumed that 32% of all deaths around worldwide are due to different types of CVDs.Advanced noticing and realizing heart diseases can be a boom to patients as they get a chance for switching their habits and life styles to a more healthy way and thus they can save their lives.Scientific researchers all over the world have been working on creating a much more intelligent decision support model for the early prediction of CVD.Healthcare people around the globe collects heart disease datasets .With the help of already available data and application of machine learning algorithms can be a cathartic feeling to the medical area.One of the main reasons of failure of intelligent prediction system is the inaccurate feature set and suffering of high overfitting and low variance.In order to predict the heart health of a patient in a more effective way,some machine learning models can be combined for better performance.Principal Component Analysis(PCA) is used to control the dimensionality of attributes.This paper mainly focuses on voting ensemble method for improving the performance of individual classifiers.
Page(s):
7381-7389
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 24, Year: 2022
Keywords:
Machine learning
,
PCA
,
DT
,
ensemble
,
LR
,
Voting
References:
References are not available for this document.
Citations
Citations are not available for this document.