Obesity risk factors ranking using multi-task learning

Abstract

Obesity is one of the leading preventable causes of death in the United States (U.S.). Risk factor analysis is a pro­cess to identify and understand the risk factors contributing to a particular disease, and is an imperative component in the de­velopment of efficient and effective prevention and intervention efforts. Most existing methods usually aim to build a one-size-fits-all model to identify the risk factors at the population-level. However, this type of methods does not take into consideration of heterogeneity in the population. To overcome this limitation, we formulate the subpopulation specific obesity risk factors ranking problem, under the framework of multi-task learning (MTL), to identify a ranked list of obesity risk factors for each subpopulation (task) simultaneously with utilizing appropriate shared information across tasks. By synchronously learning multiple related tasks, MTL provides a paradigm to rank risk factors both at the subpopulation and population-levels.