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Coronary Artery Disease I: Introduction01:30

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Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis.

Jae Kwon Kim1, Sanggil Kang1

  • 1Department of Computer Engineering, Inha University, Incheon, Republic of Korea.

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This study introduces a new neural network model (NN-FCA) for predicting coronary heart disease (CHD) risk. The NN-FCA model demonstrated superior accuracy compared to the Framingham risk score in a Korean population.

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Area of Science:

  • Machine Learning
  • Cardiovascular Disease Prediction
  • Artificial Intelligence in Healthcare

Background:

  • Neural networks (NN) are widely used for coronary heart disease (CHD) prediction.
  • However, the "black-box" nature of NN limits medical expert satisfaction with predictive performance.

Purpose of the Study:

  • To develop an improved NN-based prediction model for CHD risk.
  • To address the limitations of traditional NN models by incorporating feature correlation analysis.

Main Methods:

  • A two-stage approach was developed: feature selection and feature correlation analysis (NN-FCA).
  • Feature selection ranks predictors by importance for CHD risk.
  • Feature correlation analysis identifies relationships between predictors and NN outputs.

Main Results:

  • The study evaluated 4146 individuals from a Korean dataset.
  • The NN-FCA model achieved a significantly higher area under the receiver operating characteristic (ROC) curve (0.749 ± 0.010) compared to the Framingham risk score (FRS) (0.393 ± 0.010).

Conclusions:

  • The proposed NN-FCA model offers superior CHD risk prediction accuracy compared to the FRS.
  • NN-FCA provides a more interpretable and accurate method for predicting CHD risk in the Korean population.