Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection

Antony B. Almonacid; Ciro Rodriguez; Yuri Pomachagua; DIego Rodriguez

doi:10.1109/CICN51697.2021.9574662

Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection

Antony B. Almonacid, Ciro Rodriguez, Yuri Pomachagua, DIego Rodriguez

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

2 Citas (Scopus)

Resumen

This research aims to reduce the detection time of the risk of suffering from arterial hypertension by implementing a hybrid model based on the Support Vector Machine (SVM) and Principal Component Analysis (PCA) algorithms. The proposed hybrid model was implemented from the processing of a dataset made up of 70,000 records related to characteristics such as systolic blood pressure, diastolic blood pressure, cholesterol index, glucose index, smoking and sedentary lifestyle. The methodology for the implementation of the hybrid model consisted of the stages of data collection, data exploration, data pre-processing, selection of characteristics, and implementation of the model and the validation of results. As a result of the implementation of the model, a precision level of 72.18% was obtained in relation to the detection of the risk of suffering from arterial hypertension.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	17-22
Número de páginas	6
ISBN (versión digital)	9781728176956
DOI	https://doi.org/10.1109/CICN51697.2021.9574662
Estado	Publicada - 22 set. 2021
Evento	13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 - Lima, Perú Duración: 22 set. 2021 → 23 set. 2021

Serie de la publicación

Nombre	Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

Conferencia

Conferencia	13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021
País/Territorio	Perú
Ciudad	Lima
Período	22/09/21 → 23/09/21

Nota bibliográfica

Publisher Copyright:
© 2021 IEEE.

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10.1109/CICN51697.2021.9574662

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Almonacid, A. B., Rodriguez, C., Pomachagua, Y., & Rodriguez, DI. (2021). Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection. En Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021 (pp. 17-22). (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN51697.2021.9574662

Almonacid, Antony B. ; Rodriguez, Ciro ; Pomachagua, Yuri et al. / Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection. Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 17-22 (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021).

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title = "Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection",

abstract = "This research aims to reduce the detection time of the risk of suffering from arterial hypertension by implementing a hybrid model based on the Support Vector Machine (SVM) and Principal Component Analysis (PCA) algorithms. The proposed hybrid model was implemented from the processing of a dataset made up of 70,000 records related to characteristics such as systolic blood pressure, diastolic blood pressure, cholesterol index, glucose index, smoking and sedentary lifestyle. The methodology for the implementation of the hybrid model consisted of the stages of data collection, data exploration, data pre-processing, selection of characteristics, and implementation of the model and the validation of results. As a result of the implementation of the model, a precision level of 72.18% was obtained in relation to the detection of the risk of suffering from arterial hypertension. ",

keywords = "PCA, SVM, arterial hypertension, blood presure, hybrid model",

author = "Almonacid, {Antony B.} and Ciro Rodriguez and Yuri Pomachagua and DIego Rodriguez",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 ; Conference date: 22-09-2021 Through 23-09-2021",

year = "2021",

month = sep,

day = "22",

doi = "10.1109/CICN51697.2021.9574662",

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series = "Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021",

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Almonacid, AB, Rodriguez, C, Pomachagua, Y & Rodriguez, DI 2021, Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection. En Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021, Institute of Electrical and Electronics Engineers Inc., pp. 17-22, 13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021, Lima, Perú, 22/09/21. https://doi.org/10.1109/CICN51697.2021.9574662

Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection. / Almonacid, Antony B.; Rodriguez, Ciro; Pomachagua, Yuri et al.
Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 17-22 (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021).

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

TY - GEN

T1 - Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection

AU - Almonacid, Antony B.

AU - Rodriguez, Ciro

AU - Pomachagua, Yuri

AU - Rodriguez, DIego

PY - 2021/9/22

Y1 - 2021/9/22

N2 - This research aims to reduce the detection time of the risk of suffering from arterial hypertension by implementing a hybrid model based on the Support Vector Machine (SVM) and Principal Component Analysis (PCA) algorithms. The proposed hybrid model was implemented from the processing of a dataset made up of 70,000 records related to characteristics such as systolic blood pressure, diastolic blood pressure, cholesterol index, glucose index, smoking and sedentary lifestyle. The methodology for the implementation of the hybrid model consisted of the stages of data collection, data exploration, data pre-processing, selection of characteristics, and implementation of the model and the validation of results. As a result of the implementation of the model, a precision level of 72.18% was obtained in relation to the detection of the risk of suffering from arterial hypertension.

AB - This research aims to reduce the detection time of the risk of suffering from arterial hypertension by implementing a hybrid model based on the Support Vector Machine (SVM) and Principal Component Analysis (PCA) algorithms. The proposed hybrid model was implemented from the processing of a dataset made up of 70,000 records related to characteristics such as systolic blood pressure, diastolic blood pressure, cholesterol index, glucose index, smoking and sedentary lifestyle. The methodology for the implementation of the hybrid model consisted of the stages of data collection, data exploration, data pre-processing, selection of characteristics, and implementation of the model and the validation of results. As a result of the implementation of the model, a precision level of 72.18% was obtained in relation to the detection of the risk of suffering from arterial hypertension.

KW - PCA

KW - SVM

KW - arterial hypertension

KW - blood presure

KW - hybrid model

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U2 - 10.1109/CICN51697.2021.9574662

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M3 - Contribución a la conferencia

AN - SCOPUS:85119254468

T3 - Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

SP - 17

EP - 22

BT - Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021

Y2 - 22 September 2021 through 23 September 2021

ER -

Almonacid AB, Rodriguez C, Pomachagua Y, Rodriguez DI. Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection. En Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 17-22. (Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021). doi: 10.1109/CICN51697.2021.9574662

Hybrid Model based on Support Vector Machine and Principal Component Analysis Applied to Arterial Hypertension Detection

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