Multi Model Data Mining Approach for Heart Failure Prediction

Author : Priyanka H U, Vivek R
Volume, Issue, Month, Year : Volume 6, Number 5, September, 2016

ABSTRACT

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. 

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EVALUATION OF A NEW INCREMENTAL CLASSIFICATION TREE ALGORITHM FOR MINING HIGH SPEED DATA STREAMS

Author : N. Sivakumar
Volume, Issue, Month, Year : 3, 3, September, 2016

ABSTRACT

Abstract—A new model for online machine learning process of high speed data stream is proposed,to

minimize the severe restrictions associated with the existing computer learning algorithms. Most of the existing models have three principle steps. In the first step, the system would create a model incrementally. In the second step the time taken by the examples to complete a prescribed procedure with their arrival speed is computed. In the third and final step of the model the size of memory required for computation is predicted in advance. To overcome these restrictions we proposed this new data stream classification algorithm, where the data can be partitioned into stream of trees. In this algorithm, the new data set can be updated with the existing tree. This algorithm, called incremental classification tree algorithm, is proved to be an excellent solution for processing larger data streams. In this paper, we present the experimental results of our new algorithm and prove that our method would eradicate the problems of the existing method.

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http://allconferencecfpalerts.com/cfp/view-paper.php?eno=39