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On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders

This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require on...

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Detalles Bibliográficos
Autores principales: Ospina, Raydonal, Ferreira, Adenice G. O., de Oliveira, Hélio M., Leiva, Víctor, Castro, Cecilia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604302/
https://www.ncbi.nlm.nih.gov/pubmed/37892978
http://dx.doi.org/10.3390/biomedicines11102604
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author Ospina, Raydonal
Ferreira, Adenice G. O.
de Oliveira, Hélio M.
Leiva, Víctor
Castro, Cecilia
author_facet Ospina, Raydonal
Ferreira, Adenice G. O.
de Oliveira, Hélio M.
Leiva, Víctor
Castro, Cecilia
author_sort Ospina, Raydonal
collection PubMed
description This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository. Our findings highlight the ability of machine learning algorithms to not only streamline diagnostic procedures but also reduce diagnostic errors and the dependency on extensive clinical testing. Three key features—mean arterial pressure, pulsatile blood pressure index, and resistance-compliance indicator—were found to significantly improve the accuracy of machine learning algorithms in binary heart disease classification. Logistic regression achieved the highest average accuracy among the examined classifiers when utilizing these features. While such novel indicators contribute substantially to the classification process, they should be integrated into a broader diagnostic framework that includes comprehensive patient evaluations and medical expertise. Therefore, the present study offers valuable insights for leveraging data science techniques in the diagnosis and management of cardiovascular diseases.
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spelling pubmed-106043022023-10-28 On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders Ospina, Raydonal Ferreira, Adenice G. O. de Oliveira, Hélio M. Leiva, Víctor Castro, Cecilia Biomedicines Article This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository. Our findings highlight the ability of machine learning algorithms to not only streamline diagnostic procedures but also reduce diagnostic errors and the dependency on extensive clinical testing. Three key features—mean arterial pressure, pulsatile blood pressure index, and resistance-compliance indicator—were found to significantly improve the accuracy of machine learning algorithms in binary heart disease classification. Logistic regression achieved the highest average accuracy among the examined classifiers when utilizing these features. While such novel indicators contribute substantially to the classification process, they should be integrated into a broader diagnostic framework that includes comprehensive patient evaluations and medical expertise. Therefore, the present study offers valuable insights for leveraging data science techniques in the diagnosis and management of cardiovascular diseases. MDPI 2023-09-22 /pmc/articles/PMC10604302/ /pubmed/37892978 http://dx.doi.org/10.3390/biomedicines11102604 Text en © 2023 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
Ospina, Raydonal
Ferreira, Adenice G. O.
de Oliveira, Hélio M.
Leiva, Víctor
Castro, Cecilia
On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders
title On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders
title_full On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders
title_fullStr On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders
title_full_unstemmed On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders
title_short On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders
title_sort on the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604302/
https://www.ncbi.nlm.nih.gov/pubmed/37892978
http://dx.doi.org/10.3390/biomedicines11102604
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