Poisson Mixture Regression Models for Heart Disease Prediction
Joint Authors
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-23
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency.
This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models.
Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models.
Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value.
Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available.
It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
American Psychological Association (APA)
Mufudza, Chipo& Erol, Hamza. 2016. Poisson Mixture Regression Models for Heart Disease Prediction. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1100127
Modern Language Association (MLA)
Mufudza, Chipo& Erol, Hamza. Poisson Mixture Regression Models for Heart Disease Prediction. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1100127
American Medical Association (AMA)
Mufudza, Chipo& Erol, Hamza. Poisson Mixture Regression Models for Heart Disease Prediction. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1100127
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100127