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Deep Learning to Improve Heart Disease Risk Prediction

  • Shelda SajeevEmail author
  • Anthony Maeder
  • Stephanie Champion
  • Alline Beleigoli
  • Cheng Ton
  • Xianglong Kong
  • Minglei Shu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11794)

Abstract

Disease prediction based on modeling the correlations between compounded indicator factors is a widely used technique in high incidence chronic disease prevention diagnosis. Predictive models based on personal health information have been developed historically by using simple regression fitting over relatively few factors. Regression approaches have been favored in previous prediction modeling approaches because they are simplest and do not assume any non-linearity in the model for contributions of the chosen factors. In practice, many factors are correlated and have underlying non-linear relationships to the predicted outcome. Deep learning offers a means to construct a more complex modeling approach, along with automation and adaptation. The aim of this paper is to assess the ability of a deep learning model to predict the heart disease incidence using a common benchmark dataset (University of California, Irvine (UCI) dataset). The performance of deep learning model has been compared with four popular machine learning models (two linear and two nonlinear) in predicting the incidence of heart disease using data from 567 participants from two cohorts taken from UCI database. The deep learning model was able to achieve the best accuracy of 94% and an AUC score of 0.964 when compared to other models. The performance of deep learning and nonlinear machine learning models was significantly better compared to the linear machine learning models with increase in the dataset size.

Keywords

Cardiovascular disease Risk factors Risk prediction Machine learning Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shelda Sajeev
    • 1
    Email author
  • Anthony Maeder
    • 1
  • Stephanie Champion
    • 1
  • Alline Beleigoli
    • 1
  • Cheng Ton
    • 2
  • Xianglong Kong
    • 3
  • Minglei Shu
    • 3
  1. 1.Flinders Digital Health Research Centre, School of Nursing and Health SciencesFlinders UniversityAdelaideAustralia
  2. 2.Department of Big Data Engineering Technology Research Center of E-GovernmentJinanChina
  3. 3.Shandong Computer Science Center, Shandong Provincial Key Laboratory of Computer NetworksQilu University of TechnologyJinanChina

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