  Developing predictive modelling solutions for risk estimation is extremely
challenging in health-care informatics. Risk estimation involves integration of
heterogeneous clinical sources having different representation from different
health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time.
Therefore, distributed and parallel computing tools collectively termed big
data tools are in need which can synthesize and assist the physician to make
right clinical decisions. In this work we propose multi-model predictive
architecture, a novel approach for combining the predictive ability of multiple
models for better prediction accuracy. We demonstrate the effectiveness and
efficiency of the proposed work on data from Framingham Heart study. Results
show that the proposed multi-model predictive architecture is able to provide
better accuracy than best model approach. By modelling the error of predictive
models we are able to choose sub set of models which yields accurate results.
More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
