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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

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 originalInglés
Título de la publicación alojadaProceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas17-22
Número de páginas6
ISBN (versión digital)9781728176956
DOI
EstadoPublicada - 22 sep 2021
Publicado de forma externa
Evento13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 - Lima, Perú
Duración: 22 sep 202123 sep 2021

Serie de la publicación

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

Conferencia

Conferencia13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021
País/TerritorioPerú
CiudadLima
Período22/09/2123/09/21

Nota bibliográfica

Publisher Copyright:
© 2021 IEEE.

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