Poisson Mixture Regression Models for Heart Disease Prediction

Abstract

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.

Heart Disease Diagnosis via Nonparametric Mixture Models

“…In parametric mixture models, components and clusters are assumed to be same with the same parametric distribution. Although the best model nonparametrically for heart disease has 2 clusters and 2 components, same results as produced by [7] parametrically, the nonparametric model analysis in section 3.4 shows that the number of components is not always the same with the number of clusters. Thus, the number of components does not always correspond to the number of clusters as normally assumed in parametric mixture models but different number of components can correspond to different number of clusters being produced.…”

“…It can be deduced that individuals exposed to heart disease risks can be classified into 2 categories depending on which risks are they exposed to at a given time. This can be high risk and low risk individuals as observed by [7]…”

“…Density based clustering can be done both nonparametrically and parametrically as explained by [5,6]. In particular it was observed that heart disease can be predicted via two risk groups namely high and low risk groups via Poisson mixture regression model as explained in [7]. Nonparametric mixtures models however, identify clusters as regions of high density separated by regions of low densities which can be done by identifying local maximum values of the estimated density or modes of the data.…”

“…In this paper, clusters were identified nonparametrically using graph theory producing same results with [7] were Poisson mixture regression models were used. We recommend to data scientists therefore that a different model either with more attributes or that uses different clustering methods like splines be implemented to check the possible number of clusters that can be produced.…”

Age Structured Mixture Model for Early COVID-19 Spread: A Zimbabwean Risk Factor Analysis

“…We intend to explore the effects of Age and location differently to come up with a holistic understanding of the age structure and location on the spread in COVID-19 in Zimbabwe. GLM Mixture models allows us to measure risk factors by considering heterogeneous risk groups comprising of similar individual attributes as in [10]. The groupings will also enable us to infer into level of risk (high, medium or low) based on their individual composition.…”