Regresión logística: un ejemplo para la predicción de infartos

Henry Silva-Marchan; Gerardo Ortiz-Castro; Oscar Jhan Marcos Peña-Cáceres; Manuel Alejandro More-More

Regresión logística: un ejemplo para la predicción de infartos

Henry Silva-Marchan, Gerardo Ortiz-Castro, Oscar Jhan Marcos Peña-Cáceres, Manuel Alejandro More-More

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

Technological advances have allowed the development and availability of specialized tools for the use of historical data. In many cases, these tools are used for decision-making in multidisciplinary institutions that require support for the development of their activities, particularly in the health sector. The purpose of this study is to use a machine learning algorithm to predict heart attacks using demographic and family health data. The methodology focused on the extraction of open data from the Demographic and Family Health Survey (ENDES) applied in 2021 in Peru, characterization and execution of machine learning techniques using Orange Data Mining software. In this first approach, the results show that the logistic regression model has an accuracy of 0.99% on the prediction of heart attacks under the use of ENDES Peru 2021 data. For future studies, it is suggested to incorporate unstructured data such as text documents, sensor data and images to strengthen the reliability of the model.

Título traducido de la contribución	Logistic regression: an example for infarct prediction
Idioma original	Español
Título de la publicación alojada	Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology
Subtítulo de la publicación alojada	Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development, LACCEI 2023
Editores	Maria M. Larrondo Petrie, Jose Texier, Rodolfo Andres Rivas Matta
Editorial	Latin American and Caribbean Consortium of Engineering Institutions
ISBN (versión digital)	9786289520743
Estado	Publicada - 2023
Publicado de forma externa	Sí
Evento	21st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023 - Buenos Aires, Argentina Duración: 19 jul. 2023 → 21 jul. 2023

Serie de la publicación

Nombre	Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volumen	2023-July
ISSN (versión digital)	2414-6390

Conferencia

Conferencia	21st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023
País/Territorio	Argentina
Ciudad	Buenos Aires
Período	19/07/23 → 21/07/23

Nota bibliográfica

Publisher Copyright:
© 2023 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Palabras clave

ENDES
Infarction
logistic regression
Machine learning

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Enlace a la publicación en Scopus

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Silva-Marchan, H., Ortiz-Castro, G., Peña-Cáceres, O. J. M., & More-More, M. A. (2023). Regresión logística: un ejemplo para la predicción de infartos. En M. M. Larrondo Petrie, J. Texier, & R. A. R. Matta (Eds.), Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology: Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development, LACCEI 2023 (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2023-July). Latin American and Caribbean Consortium of Engineering Institutions.

Silva-Marchan, Henry ; Ortiz-Castro, Gerardo ; Peña-Cáceres, Oscar Jhan Marcos et al. / Regresión logística : un ejemplo para la predicción de infartos. Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology: Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development, LACCEI 2023. editor / Maria M. Larrondo Petrie ; Jose Texier ; Rodolfo Andres Rivas Matta. Latin American and Caribbean Consortium of Engineering Institutions, 2023. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

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abstract = "Technological advances have allowed the development and availability of specialized tools for the use of historical data. In many cases, these tools are used for decision-making in multidisciplinary institutions that require support for the development of their activities, particularly in the health sector. The purpose of this study is to use a machine learning algorithm to predict heart attacks using demographic and family health data. The methodology focused on the extraction of open data from the Demographic and Family Health Survey (ENDES) applied in 2021 in Peru, characterization and execution of machine learning techniques using Orange Data Mining software. In this first approach, the results show that the logistic regression model has an accuracy of 0.99% on the prediction of heart attacks under the use of ENDES Peru 2021 data. For future studies, it is suggested to incorporate unstructured data such as text documents, sensor data and images to strengthen the reliability of the model.",

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Silva-Marchan, H, Ortiz-Castro, G, Peña-Cáceres, OJM & More-More, MA 2023, Regresión logística: un ejemplo para la predicción de infartos. En MM Larrondo Petrie, J Texier & RAR Matta (eds.), Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology: Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development, LACCEI 2023. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, vol. 2023-July, Latin American and Caribbean Consortium of Engineering Institutions, 21st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023, Buenos Aires, Argentina, 19/07/23.

Regresión logística: un ejemplo para la predicción de infartos. / Silva-Marchan, Henry; Ortiz-Castro, Gerardo; Peña-Cáceres, Oscar Jhan Marcos et al.
Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology: Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development, LACCEI 2023. ed. / Maria M. Larrondo Petrie; Jose Texier; Rodolfo Andres Rivas Matta. Latin American and Caribbean Consortium of Engineering Institutions, 2023. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2023-July).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Regresión logística

T2 - 21st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023

AU - Silva-Marchan, Henry

AU - Ortiz-Castro, Gerardo

AU - Peña-Cáceres, Oscar Jhan Marcos

AU - More-More, Manuel Alejandro

PY - 2023

Y1 - 2023

N2 - Technological advances have allowed the development and availability of specialized tools for the use of historical data. In many cases, these tools are used for decision-making in multidisciplinary institutions that require support for the development of their activities, particularly in the health sector. The purpose of this study is to use a machine learning algorithm to predict heart attacks using demographic and family health data. The methodology focused on the extraction of open data from the Demographic and Family Health Survey (ENDES) applied in 2021 in Peru, characterization and execution of machine learning techniques using Orange Data Mining software. In this first approach, the results show that the logistic regression model has an accuracy of 0.99% on the prediction of heart attacks under the use of ENDES Peru 2021 data. For future studies, it is suggested to incorporate unstructured data such as text documents, sensor data and images to strengthen the reliability of the model.

AB - Technological advances have allowed the development and availability of specialized tools for the use of historical data. In many cases, these tools are used for decision-making in multidisciplinary institutions that require support for the development of their activities, particularly in the health sector. The purpose of this study is to use a machine learning algorithm to predict heart attacks using demographic and family health data. The methodology focused on the extraction of open data from the Demographic and Family Health Survey (ENDES) applied in 2021 in Peru, characterization and execution of machine learning techniques using Orange Data Mining software. In this first approach, the results show that the logistic regression model has an accuracy of 0.99% on the prediction of heart attacks under the use of ENDES Peru 2021 data. For future studies, it is suggested to incorporate unstructured data such as text documents, sensor data and images to strengthen the reliability of the model.

KW - ENDES

KW - Infarction

KW - logistic regression

KW - Machine learning

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M3 - Contribución a la conferencia

AN - SCOPUS:85172318463

T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology

BT - Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology

A2 - Larrondo Petrie, Maria M.

A2 - Texier, Jose

A2 - Matta, Rodolfo Andres Rivas

PB - Latin American and Caribbean Consortium of Engineering Institutions

Y2 - 19 July 2023 through 21 July 2023

ER -

Silva-Marchan H, Ortiz-Castro G, Peña-Cáceres OJM, More-More MA. Regresión logística: un ejemplo para la predicción de infartos. En Larrondo Petrie MM, Texier J, Matta RAR, editores, Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology: Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development, LACCEI 2023. Latin American and Caribbean Consortium of Engineering Institutions. 2023. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

Regresión logística: un ejemplo para la predicción de infartos

Resumen

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