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An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning
Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician’s skill and judgment. Several studies have shown promising results in automatically detecting cardiovascular...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854602/ https://www.ncbi.nlm.nih.gov/pubmed/36671616 http://dx.doi.org/10.3390/bioengineering10010045 |
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author | Fuadah, Yunendah Nur Pramudito, Muhammad Adnan Lim, Ki Moo |
author_facet | Fuadah, Yunendah Nur Pramudito, Muhammad Adnan Lim, Ki Moo |
author_sort | Fuadah, Yunendah Nur |
collection | PubMed |
description | Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician’s skill and judgment. Several studies have shown promising results in automatically detecting cardiovascular disorders based on heart-sound signals. However, the accuracy performance needs to be enhanced as automated heart-sound classification aids in the early detection and prevention of the dangerous effects of cardiovascular problems. In this study, an optimal heart-sound classification method based on machine learning technologies for cardiovascular disease prediction is performed. It consists of three steps: pre-processing that sets the 5 s duration of the PhysioNet Challenge 2016 and 2022 datasets, feature extraction using Mel frequency cepstrum coefficients (MFCC), and classification using grid search for hyperparameter tuning of several classifier algorithms including k-nearest neighbor (K-NN), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The five-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained classification accuracy of 95.78% and 76.31%, which was assessed using PhysioNet Challenge 2016 and 2022, respectively. The findings demonstrate that the suggested approach obtained excellent classification results using PhysioNet Challenge 2016 and showed promising results using PhysioNet Challenge 2022. Therefore, the proposed method has been potentially developed as an additional tool to facilitate the medical practitioner in diagnosing the abnormality of the heart sound. |
format | Online Article Text |
id | pubmed-9854602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98546022023-01-21 An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning Fuadah, Yunendah Nur Pramudito, Muhammad Adnan Lim, Ki Moo Bioengineering (Basel) Article Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician’s skill and judgment. Several studies have shown promising results in automatically detecting cardiovascular disorders based on heart-sound signals. However, the accuracy performance needs to be enhanced as automated heart-sound classification aids in the early detection and prevention of the dangerous effects of cardiovascular problems. In this study, an optimal heart-sound classification method based on machine learning technologies for cardiovascular disease prediction is performed. It consists of three steps: pre-processing that sets the 5 s duration of the PhysioNet Challenge 2016 and 2022 datasets, feature extraction using Mel frequency cepstrum coefficients (MFCC), and classification using grid search for hyperparameter tuning of several classifier algorithms including k-nearest neighbor (K-NN), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The five-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained classification accuracy of 95.78% and 76.31%, which was assessed using PhysioNet Challenge 2016 and 2022, respectively. The findings demonstrate that the suggested approach obtained excellent classification results using PhysioNet Challenge 2016 and showed promising results using PhysioNet Challenge 2022. Therefore, the proposed method has been potentially developed as an additional tool to facilitate the medical practitioner in diagnosing the abnormality of the heart sound. MDPI 2022-12-29 /pmc/articles/PMC9854602/ /pubmed/36671616 http://dx.doi.org/10.3390/bioengineering10010045 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fuadah, Yunendah Nur Pramudito, Muhammad Adnan Lim, Ki Moo An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning |
title | An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning |
title_full | An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning |
title_fullStr | An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning |
title_full_unstemmed | An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning |
title_short | An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning |
title_sort | optimal approach for heart sound classification using grid search in hyperparameter optimization of machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854602/ https://www.ncbi.nlm.nih.gov/pubmed/36671616 http://dx.doi.org/10.3390/bioengineering10010045 |
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