The Application of Mixture Modeling and Information Criteria for Discovering Patterns of Coronary Heart Disease
Quantitative Methods, Unsupervised Learning, Finite Mixture Models, Patterns in Continuous Data, Theoretical Information Criteria, Simulation experiments, Coronary Heart Disease
This paper’s purpose is twofold: first it addresses the adequacy of some theoretical information criteria when using finite mixture modelling (unsupervised learning) on discovering patterns in continuous data; second, we aim to apply these models and BIC to discover patterns of coronary heart disease. In order to select among several information criteria, which may support the selection of the correct number of clusters, we conduct a simulation study, in order to determine which information criteria are more appropriate for mixture model selection when considering data sets with only continuous clustering base variables. As a result, the criterion BIC shows a better performance, that is, it indicates the correct number of the simulated cluster structures more often. When applied to discover patterns of Coronary Heart Disease, it performed well, discovering the known pattern of data.