Targeted Local Support Vector Machine for Age-Dependent Classification.

ABSTRACT: We develop methods to accurately predict whether pre-symptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example age. To identify effective prediction rules using nonparametric decision functions, standard statistical learning approaches treat markers with clear biological importance (e.g., age) and other markers without prior knowledge on disease etiology interchangeably as input variables. Therefore, these approaches may be inadequate in singling out and preserving the effects from the biologically important va

SUBMITTER: Chen T 

PROVIDER: S-EPMC4183366 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature