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Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging

Myocardial perfusion imaging (MPI) is an essential tool used to diagnose and manage patients with suspected or known coronary artery disease. Additionally, the General Data Protection Regulation (GDPR) represents a milestone about individuals' data security concerns. On the other hand, Machine...

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Autores principales: de Souza Filho, Erito Marques, Fernandes, Fernando de Amorim, Portela, Maria Gabriela Ribeiro, Newlands, Pedro Heliodoro, de Carvalho, Lucas Nunes Dalbonio, dos Santos, Tadeu Francisco, dos Santos, Alair Augusto Sarmet M. D., Mesquita, Evandro Tinoco, Seixas, Flávio Luiz, Mesquita, Claudio Tinoco, Gismondi, Ronaldo Altenburg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8585770/
https://www.ncbi.nlm.nih.gov/pubmed/34778403
http://dx.doi.org/10.3389/fcvm.2021.741679
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author de Souza Filho, Erito Marques
Fernandes, Fernando de Amorim
Portela, Maria Gabriela Ribeiro
Newlands, Pedro Heliodoro
de Carvalho, Lucas Nunes Dalbonio
dos Santos, Tadeu Francisco
dos Santos, Alair Augusto Sarmet M. D.
Mesquita, Evandro Tinoco
Seixas, Flávio Luiz
Mesquita, Claudio Tinoco
Gismondi, Ronaldo Altenburg
author_facet de Souza Filho, Erito Marques
Fernandes, Fernando de Amorim
Portela, Maria Gabriela Ribeiro
Newlands, Pedro Heliodoro
de Carvalho, Lucas Nunes Dalbonio
dos Santos, Tadeu Francisco
dos Santos, Alair Augusto Sarmet M. D.
Mesquita, Evandro Tinoco
Seixas, Flávio Luiz
Mesquita, Claudio Tinoco
Gismondi, Ronaldo Altenburg
author_sort de Souza Filho, Erito Marques
collection PubMed
description Myocardial perfusion imaging (MPI) is an essential tool used to diagnose and manage patients with suspected or known coronary artery disease. Additionally, the General Data Protection Regulation (GDPR) represents a milestone about individuals' data security concerns. On the other hand, Machine Learning (ML) has had several applications in the most diverse knowledge areas. It is conceived as a technology with huge potential to revolutionize health care. In this context, we developed ML models to evaluate their ability to distinguish an individual's sex from MPI assessment. We used 260 polar maps (140 men/120 women) to train ML algorithms from a database of patients referred to a university hospital for clinically indicated MPI from January 2016 to December 2018. We tested 07 different ML models, namely, Classification and Regression Tree (CART), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Adaptive Boosting (AB), Random Forests (RF) and, Gradient Boosting (GB). We used a cross-validation strategy. Our work demonstrated that ML algorithms could perform well in assessing the sex of patients undergoing myocardial scintigraphy exams. All the models had accuracy greater than 82%. However, only SVM achieved 90%. KNN, RF, AB, GB had, respectively, 88, 86, 85, 83%. Accuracy standard deviation was lower in KNN, AB, and RF (0.06). SVM and RF had had the best area under the receiver operating characteristic curve (0.93), followed by GB (0.92), KNN (0.91), AB, and NB (0.9). SVM and AB achieved the best precision. Our results bring some challenges regarding the autonomy of patients who wish to keep sex information confidential and certainly add greater complexity to the debate about what data should be considered sensitive to the light of the GDPR.
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spelling pubmed-85857702021-11-13 Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging de Souza Filho, Erito Marques Fernandes, Fernando de Amorim Portela, Maria Gabriela Ribeiro Newlands, Pedro Heliodoro de Carvalho, Lucas Nunes Dalbonio dos Santos, Tadeu Francisco dos Santos, Alair Augusto Sarmet M. D. Mesquita, Evandro Tinoco Seixas, Flávio Luiz Mesquita, Claudio Tinoco Gismondi, Ronaldo Altenburg Front Cardiovasc Med Cardiovascular Medicine Myocardial perfusion imaging (MPI) is an essential tool used to diagnose and manage patients with suspected or known coronary artery disease. Additionally, the General Data Protection Regulation (GDPR) represents a milestone about individuals' data security concerns. On the other hand, Machine Learning (ML) has had several applications in the most diverse knowledge areas. It is conceived as a technology with huge potential to revolutionize health care. In this context, we developed ML models to evaluate their ability to distinguish an individual's sex from MPI assessment. We used 260 polar maps (140 men/120 women) to train ML algorithms from a database of patients referred to a university hospital for clinically indicated MPI from January 2016 to December 2018. We tested 07 different ML models, namely, Classification and Regression Tree (CART), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Adaptive Boosting (AB), Random Forests (RF) and, Gradient Boosting (GB). We used a cross-validation strategy. Our work demonstrated that ML algorithms could perform well in assessing the sex of patients undergoing myocardial scintigraphy exams. All the models had accuracy greater than 82%. However, only SVM achieved 90%. KNN, RF, AB, GB had, respectively, 88, 86, 85, 83%. Accuracy standard deviation was lower in KNN, AB, and RF (0.06). SVM and RF had had the best area under the receiver operating characteristic curve (0.93), followed by GB (0.92), KNN (0.91), AB, and NB (0.9). SVM and AB achieved the best precision. Our results bring some challenges regarding the autonomy of patients who wish to keep sex information confidential and certainly add greater complexity to the debate about what data should be considered sensitive to the light of the GDPR. Frontiers Media S.A. 2021-10-29 /pmc/articles/PMC8585770/ /pubmed/34778403 http://dx.doi.org/10.3389/fcvm.2021.741679 Text en Copyright © 2021 Souza Filho, Fernandes, Portela, Newlands, Carvalho, Santos, Santos, Mesquita, Seixas, Mesquita and Gismondi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
de Souza Filho, Erito Marques
Fernandes, Fernando de Amorim
Portela, Maria Gabriela Ribeiro
Newlands, Pedro Heliodoro
de Carvalho, Lucas Nunes Dalbonio
dos Santos, Tadeu Francisco
dos Santos, Alair Augusto Sarmet M. D.
Mesquita, Evandro Tinoco
Seixas, Flávio Luiz
Mesquita, Claudio Tinoco
Gismondi, Ronaldo Altenburg
Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging
title Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging
title_full Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging
title_fullStr Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging
title_full_unstemmed Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging
title_short Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging
title_sort machine learning algorithms to detect sex in myocardial perfusion imaging
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8585770/
https://www.ncbi.nlm.nih.gov/pubmed/34778403
http://dx.doi.org/10.3389/fcvm.2021.741679
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