Multi Model Data mining approach for Heart failure prediction

TL;DR

This paper introduces a multi-model predictive architecture that combines multiple models to improve heart failure prediction accuracy using heterogeneous health data, demonstrating superior results over single models.

Contribution

The paper presents a novel multi-model predictive architecture that integrates diverse models and multi-level data mining to enhance prediction accuracy in healthcare risk estimation.

Findings

Multi-model architecture outperforms single best models in accuracy.

Error modeling helps select optimal subset of models.

Multi-level mining enhances predictive performance.

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.