Heart Disease Diagnosis via Nonparametric Mixture Models

Abstract

Aims/Objectives: Effective and efficient heart disease prediction via nonparametric mixture regression models. Data Source: Data used in this paper is from the UCI database of the Cleveland Clinic Foundation for heart disease. The original data source contains 76 raw attributes with 303 observations each. For the purpose of this paper only 14 attributes were used as explained in section 4. Methodology: Cluster analysis was applied via mixture models in the form of Nonparametric Density-based models. The clusters were identified using a graph theory based technique. Voronoi diagrams were used and and their distributions were estimated nonparametrically through a mixture model with Gaussian kernels. The optimal number of clusters and components of the