A model-based approach to Bayesian classification with applications to predicting pregnancy outcomes from longitudinal Beta-hCG profiles

This paper discusses Bayesian statistical methods for the classification of observations into two or more groups based on hierarchical models for nonlinear longitudinal profiles.Parameter estimation for a discriminant model that classifies individuals into distinct predefined groups or populations uses appropriate posterior simulation schemes.The methods are illustrated with data from a study involving 173 pregnant women.The main objective in this study is to predict normal versus abnormal pregnancy outcomes from beta human chorionic gonadotropin data available at early stages of pregnancy.