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Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods
The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to impl...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7999739/ https://www.ncbi.nlm.nih.gov/pubmed/33809283 http://dx.doi.org/10.3390/healthcare9030317 |
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author | Soto-Murillo, Manuel A. Galván-Tejada, Jorge I. Galván-Tejada, Carlos E. Celaya-Padilla, Jose M. Luna-García, Huizilopoztli Magallanes-Quintanar, Rafael Gutiérrez-García, Tania A. Gamboa-Rosales, Hamurabi |
author_facet | Soto-Murillo, Manuel A. Galván-Tejada, Jorge I. Galván-Tejada, Carlos E. Celaya-Padilla, Jose M. Luna-García, Huizilopoztli Magallanes-Quintanar, Rafael Gutiérrez-García, Tania A. Gamboa-Rosales, Hamurabi |
author_sort | Soto-Murillo, Manuel A. |
collection | PubMed |
description | The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems. |
format | Online Article Text |
id | pubmed-7999739 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79997392021-03-28 Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods Soto-Murillo, Manuel A. Galván-Tejada, Jorge I. Galván-Tejada, Carlos E. Celaya-Padilla, Jose M. Luna-García, Huizilopoztli Magallanes-Quintanar, Rafael Gutiérrez-García, Tania A. Gamboa-Rosales, Hamurabi Healthcare (Basel) Article The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems. MDPI 2021-03-12 /pmc/articles/PMC7999739/ /pubmed/33809283 http://dx.doi.org/10.3390/healthcare9030317 Text en © 2021 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 (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Soto-Murillo, Manuel A. Galván-Tejada, Jorge I. Galván-Tejada, Carlos E. Celaya-Padilla, Jose M. Luna-García, Huizilopoztli Magallanes-Quintanar, Rafael Gutiérrez-García, Tania A. Gamboa-Rosales, Hamurabi Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods |
title | Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods |
title_full | Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods |
title_fullStr | Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods |
title_full_unstemmed | Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods |
title_short | Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods |
title_sort | automatic evaluation of heart condition according to the sounds emitted and implementing six classification methods |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7999739/ https://www.ncbi.nlm.nih.gov/pubmed/33809283 http://dx.doi.org/10.3390/healthcare9030317 |
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