Multi Model Data mining approach for Heart failure prediction
This work addresses risk estimation for heart failure prediction in healthcare informatics, but it appears incremental as it builds on existing multi-model methods without introducing a fundamentally new paradigm.
The authors tackled the challenge of predicting heart failure risk by integrating heterogeneous clinical data sources, proposing a multi-model predictive architecture that combines multiple models to improve accuracy, and demonstrated on Framingham Heart Study data that it outperforms the best single model approach.
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.