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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	17-22
Number of pages	6
ISBN (Electronic)	9781728176956
DOIs	https://doi.org/10.1109/CICN51697.2021.9574662
State	Published - 22 Sep 2021
Event	13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 - Lima, Peru Duration: 22 Sep 2021 → 23 Sep 2021

Publication series

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

Conference

Conference	13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021
Country/Territory	Peru
City	Lima
Period	22/09/21 → 23/09/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

PCA
SVM
arterial hypertension
blood presure
hybrid model

Access to Document

10.1109/CICN51697.2021.9574662

Cite this

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. In 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",

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day = "22",

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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. in 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, Peru, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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

PB - Institute of Electrical and Electronics Engineers Inc.

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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. In 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

Abstract

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Keywords

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