  Diabetes prediction is an important data science application in the social
healthcare domain. There exist two main challenges in the diabetes prediction
task: data heterogeneity since demographic and metabolic data are of different
types, data insufficiency since the number of diabetes cases in a single
medical center is usually limited. To tackle the above challenges, we employ
gradient boosting decision trees (GBDT) to handle data heterogeneity and
introduce multi-task learning (MTL) to solve data insufficiency. To this end,
Task-wise Split Gradient Boosting Trees (TSGB) is proposed for the multi-center
diabetes prediction task. Specifically, we firstly introduce task gain to
evaluate each task separately during tree construction, with a theoretical
analysis of GBDT's learning objective. Secondly, we reveal a problem when
directly applying GBDT in MTL, i.e., the negative task gain problem. Finally,
we propose a novel split method for GBDT in MTL based on the task gain
statistics, named task-wise split, as an alternative to standard feature-wise
split to overcome the mentioned negative task gain problem. Extensive
experiments on a large-scale real-world diabetes dataset and a commonly used
benchmark dataset demonstrate TSGB achieves superior performance against
several state-of-the-art methods. Detailed case studies further support our
analysis of negative task gain problems and provide insightful findings. The
proposed TSGB method has been deployed as an online diabetes risk assessment
software for early diagnosis.
