Hierarchical Models for Screening of Iron Deficiency Anemia

Abstract

We investigate the problem of classifying individuals based on estimated density functions for each individual. The problem is similar to conventional classification in that there is labelled training data, but different in that the underlying measurements are not feature vectors but histograms or density estimates. We describe a general framework based on probabilistic hierarchical models for modelling such data and illustrate how the model lends itself to classification. We contrast this approach with two other alternatives: (1) directly defining distance between densities using a cross-entropy distance measure, and (2) using parameters of the estimated densities as feature vectors for a standard discriminative classification framework. We evaluate all three methods on a realworld medical diagnosis problem. The hierarchical modeling and density-distance approaches are most accurate, yielding crossvalidated error rates in the range of 1 to 2%. We conclude by discussing the relative me...