Application of neural networks for the classification of blood pressure levels in patients from Ocaña – North of Santander
DOI:
https://doi.org/10.24054/rcta.v1i41.2415Keywords:
Model, Neuron, Level, Network, VoltageAbstract
The objective of this research is to model the behavior of blood pressure taking into account two factors such as age and gender in patients from the city of Ocaña - Norte de Santander. For the development of the project, the fundamental stages of data analysis are taken into account: adaptation of the database, exploratory analysis, verification of artificial intelligence models with classifying neural networks; the nature of the research is exploratory with a quantitative approach and non-experimental design. Various neural network models with different numbers of hidden layers and number of neurons were tested; it was found that the model with the highest precision was with two hidden layers of 100 neurons each, which achieved an accuracy of 87%. In conclusion, it was possible to determine a model of neural networks that, with the characteristics of gender and age, plus diastolic and systolic pressure, can classify the patient in the levels hypotension, hypertension, normal, optimal, systolic hypertension or detect any abnormality.
References
Alghamdi, A. S., Polat, K., Alghoson, A., Alshdadi, A. A., & Abd El-Latif, A. A. (2020). A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods. Applied Acoustics, 164. https://doi.org/10.1016/j.apacoust.2020.107279
Andía, M. E., Arrieta, C., & Sing Long, C. A. (2019). A conceptual guide to use and understand Big Data in clinical research. En Revista Medica Clinica Las Condes (Vol. 30, Número 1, pp. 83–94). Ediciones Doyma, S.L. https://doi.org/10.1016/j.rmclc.2018.11.003
Ávila-Tomás, J. F., Mayer-Pujadas, M. A., & Quesada-Varela, V. J. (2021). Artificial intelligence and its applications in medicine II: Current importance and practical applications. Atencion Primaria, 53(1), 81–88. https://doi.org/10.1016/j.aprim.2020.04.014
Bukhari, M. M., Alkhamees, B. F., Hussain, S., Gumaei, A., Assiri, A., & Ullah, S. S. (2021). An Improved Artificial Neural Network Model for Effective Diabetes Prediction. Complexity, 2021. https://doi.org/10.1155/2021/5525271
Delgado Karina, Ledesma Sergio, & Rostro Horacio. (2019). Análisis de electroencefalograma usando redes neuronales artificiales. Multidisciplinary Science Journal, 29, 1–24.
Esmaelpoor, J., Moradi, M. H., & Kadkhodamohammadi, A. (2020). A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals. Computers in Biology and Medicine, 120. https://doi.org/10.1016/j.compbiomed.2020.103719
Hill, B. L., Rakocz, N., Rudas, Á., Chiang, J. N., Wang, S., Hofer, I., Cannesson, M., & Halperin, E. (2021). Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-94913-y
López-Martínez, F., Núñez-Valdez, E. R., Crespo, R. G., & García-Díaz, V. (2020). An artificial neural network approach for predicting hypertension using NHANES data. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-67640-z
Martinez-Ríos, E., Montesinos, L., Alfaro-Ponce, M., & Pecchia, L. (2021). A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. En Biomedical Signal Processing and Control (Vol. 68). Elsevier Ltd. https://doi.org/10.1016/j.bspc.2021.102813
Sarmiento-Ramos, J. L. (2020). Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica. Revista UIS Ingenierías, 19(4), 1–18. https://doi.org/10.18273/revuin.v19n4-2020001
Downloads
Published
Versions
How to Cite
Issue
Section
License
Copyright (c) 2023 REVISTA COLOMBIANA DE TECNOLOGÍAS DE AVANZADA
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors publishing in this journal agree to the following conditions:
- Authors retain the moral rights of authorship and grant the journal the right of first publication of the work. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0 DEED), allowing third parties to use the work provided appropriate credit is given to the authors and the first publication in this journal. Commercial use of the work is not permitted, and derivative works cannot be created.
- Authors may enter into separate, additional contractual agreements for the non-exclusive distribution of the journal's published version of the article (for example, including it in an institutional repository or publishing it in a book), provided that the work is clearly indicated as having first been published in this journal.
- Authors are permitted and encouraged to post their work online (e.g., on institutional or personal websites) before and during the review and publication process, as it can lead to productive exchanges and greater and faster dissemination of the published work.
