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A Decision Support System for Cardiac Disease Diagnosis Based on Machine Learning Methods.

Arash Gharehbaghi1, Maria Lindén1, Ankica Babic2

  • 1Department of Innovation, Design and Technology, Mälardalen University, Sweden.

Studies in Health Technology and Informatics
|April 21, 2017
PubMed
Summary

This study introduces a novel system using heart sound analysis and a hidden Markov model to screen pediatric cardiac disease. The system demonstrates high accuracy, outperforming expert cardiologists in identifying congenital heart disease in children.

Keywords:
Hidden Markov modelcongenital heart disease screeningdecision support systemheart sound

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Artificial Intelligence in Medicine

Background:

  • Pediatric cardiac disease screening is crucial for early intervention.
  • Current auscultation methods by pediatric cardiologists have limitations.
  • A need exists for objective, accessible diagnostic tools in primary healthcare.

Purpose of the Study:

  • To develop and evaluate a decision support system for screening pediatric cardiac disease.
  • To utilize heart sound time series analysis with a hidden Markov model for disease detection.
  • To compare the system's performance against expert pediatric cardiologists.

Main Methods:

  • A processing method based on the hidden Markov model was developed for heart sound time series analysis.
  • The method generates a binary output to classify children as having heart disease or being healthy.
  • A study cohort included 90 children (35 with congenital heart disease, 55 healthy).

Main Results:

  • The system achieved an accuracy of 86.4% and a sensitivity of 85.6%.
  • Performance surpassed that of a pediatric cardiologist performing auscultation.
  • The hidden Markov model effectively extracted discriminative information from heart sound data.

Conclusions:

  • The proposed decision support system shows significant potential for screening pediatric cardiac disease.
  • The system's performance is superior to traditional auscultation by experts.
  • Implementation via mobile and web technology can create an accessible diagnostic tool for primary care.