Prioritization of Multi-Level Risk Factors for Obesity

Abstract

Obesity has become a significant threat to health. Identifying and understanding the underlying obesity risk factors (ORFs) are crucial for optimizing prevention, intervention and treatment for obesity. Most existing methodological approaches to risk factor analysis are employed within the single task learning (STL) framework to learn a ranked list of ORFs for a whole population. However, obesity is a multi-faced health outcome. Some ORFs are highly specific to a certain subpopulation and others are universal to the entire population. Multi-task learning (MTL) framework offers a solution to connect multiple related tasks. Within the MTL framework, we implement two tailor-made models, i.e., multi-task feature learning (MTFL) and clustered multi-task learning (CMTL), to conduct ORFs analysis. The former is capable of finding the universal ORFs for all subpopulations without sacrificing the uniqueness of each subpopulation. The latter uncovers the grouping structure and conducts multi-level ORFs analysis simultaneously. Experiments on a public behavioral dataset demonstrate a superior performance of our methods in prioritizing multi-level ORFs.