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An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine

Yunendah Nur Fuadah1,2, Muhammad Adnan Pramudito1, Ki Moo Lim1,3,4

  • 1Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.

Bioengineering (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

This study developed an optimal machine learning method for cardiovascular disease prediction using heart sound analysis. The approach achieved high accuracy, offering a potential tool for early detection of heart abnormalities.

Keywords:
MFCCartificial neural networksgrid searchheart sound signalk-nearest neighborrandom forestsupport vector machine

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Heart-sound auscultation is crucial for diagnosing cardiovascular disorders but relies heavily on physician expertise.
  • Automated heart sound analysis shows promise for early disease detection, but accuracy needs improvement.
  • Accurate automated classification of heart sounds can aid in preventing severe cardiovascular complications.

Purpose of the Study:

  • To develop an optimal machine learning-based method for cardiovascular disease prediction using heart sound signals.
  • To enhance the accuracy of automated heart sound classification for improved early diagnosis.

Main Methods:

  • Utilized PhysioNet Challenge 2016 and 2022 datasets with a 5-second duration for pre-processing.
  • Extracted features using Mel frequency cepstrum coefficients (MFCC).
  • Employed grid search for hyperparameter tuning of k-nearest neighbor (K-NN), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) classifiers, with five-fold cross-validation.

Main Results:

  • The best model achieved 95.78% accuracy on the PhysioNet Challenge 2016 dataset.
  • The same model obtained 76.31% accuracy on the PhysioNet Challenge 2022 dataset.
  • Demonstrated excellent classification performance on one dataset and promising results on the other.

Conclusions:

  • The proposed machine learning method shows significant potential for accurate heart sound classification.
  • This approach can serve as a valuable supplementary tool for medical practitioners in diagnosing heart sound abnormalities.
  • Further development could enhance early detection and management of cardiovascular diseases.