Targeted Local Support Vector Machine for Age-Dependent Classification.

TitleTargeted Local Support Vector Machine for Age-Dependent Classification.
Publication TypeJournal Article
Year of Publication2014
AuthorsChen, Tianle, Yuanjia Wang, Huaihou Chen, Karen Marder, and Donglin Zeng
JournalJ Am Stat Assoc
Date Published2014 Sep 01

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 variables, especially in the presence of potential noise markers. Using age as an example of a salient marker to receive special care in the analysis, we propose a local smoothing large margin classifier implemented with support vector machine (SVM) to construct effective age-dependent classification rules. The method adaptively adjusts age effect and separately tunes age and other markers to achieve optimal performance. We derive the asymptotic risk bound of the local smoothing SVM, and perform extensive simulation studies to compare with standard approaches. We apply the proposed method to two studies of premanifest Huntington's disease (HD) subjects and controls to construct age-sensitive predictive scores for the risk of HD and risk of receiving HD diagnosis during the study period.

Alternate JournalJ Am Stat Assoc
Original PublicationTargeted local support vector machine for age-dependent classification.
PubMed ID25284918
PubMed Central IDPMC4183366
Grant ListR01 CA082659 / CA / NCI NIH HHS / United States
UL1 TR001111 / TR / NCATS NIH HHS / United States
R37 GM047845 / GM / NIGMS NIH HHS / United States
R01 NS073671 / NS / NINDS NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States
R01 NS036630 / NS / NINDS NIH HHS / United States
R56 NS036630 / NS / NINDS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States