Heart Disease Diagnosis via Nonparametric Mixture Models

Effective and efficient ways to heart disease diagnosis can be improved via clustering individuals with heterogeneous characteristics to similar risk groups. This paper focuses on nonparametric density based cluster analysis on the risks of heart disease via nonparametric mixtures. Cluster density distributions for the nonparametric mixture model are done through Gaussian kernel density estimators using graph theory techniques. The cluster quality for the clusters from the models were analysed and diagnosed via a density based silhouette information criteria. Although the number of components is not assumed the same with clusters, results shows that individuals under heart disease risks can be grouped into two categories using two component model. It was also concluded that the individuals in different cluster have varying risk levels for heart disease.