Subpopulation-specific confidence designation for more informative biomedical classification.

ABSTRACT:

Objective

Although classification algorithms are promising tools to support clinical diagnosis and treatment of disease, the usual implicit assumption underlying these algorithms, that all patients are homogeneous with respect to characteristics of interest, is unsatisfactory. The objective here is to exploit the population heterogeneity reflected by characteristics that may not be apparent and thus not controlled, in order to differentiate levels of classification accuracy between subpopulations and further the goal of tailoring therapies on an individual basis.

Methods and materials

A new subpopulation-based confidence approach is developed in the context of a selective voting algorithm defined by an ensemble of convex-hull classifiers. Populations of training samples are

SUBMITTER: Zhang C 

PROVIDER: S-EPMC3727244 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature