Deep Learning to Improve Heart Disease Risk Prediction

Shelda Sajeev, Anthony Maeder, Stephanie Champion, Alline Beleigoli, Cheng Ton, Xianglong Kong, Minglei Shu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMachine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsHongen Liao, Guijin Wang, Yongpan Liu, Zijian Ding, Simone Balocco, Feng Zhang, Luc Duong, Renzo Phellan, Guillaume Zahnd, Shadi Albarqouni, Stefanie Demirci, Katharina Breininger, Stefano Moriconi, Su-Lin Lee
PublisherSpringer Gabler
Pages96-103
Number of pages8
ISBN (Print)9783030333263
DOIs
Publication statusPublished - 1 Jan 2019
Externally publishedYes
Event1st International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201913 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11794 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period13/10/1913/10/19

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Keywords

  • Cardiovascular disease
  • Deep learning
  • Machine learning
  • Risk factors
  • Risk prediction

Cite this

Sajeev, S., Maeder, A., Champion, S., Beleigoli, A., Ton, C., Kong, X., & Shu, M. (2019). Deep Learning to Improve Heart Disease Risk Prediction. In H. Liao, G. Wang, Y. Liu, Z. Ding, S. Balocco, F. Zhang, L. Duong, R. Phellan, G. Zahnd, S. Albarqouni, S. Demirci, K. Breininger, S. Moriconi, & S-L. Lee (Eds.), Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 96-103). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11794 LNCS). Springer Gabler. https://doi.org/10.1007/978-3-030-33327-0_12