Cargando…

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia

This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected,...

Descripción completa

Detalles Bibliográficos
Autores principales: Chai, Soo See, Cheah, Whye Lian, Goh, Kok Luong, Chang, Yee Hui Robin, Sim, Kwan Yong, Chin, Kim On
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8670914/
https://www.ncbi.nlm.nih.gov/pubmed/34917164
http://dx.doi.org/10.1155/2021/2794888
_version_ 1784615055696855040
author Chai, Soo See
Cheah, Whye Lian
Goh, Kok Luong
Chang, Yee Hui Robin
Sim, Kwan Yong
Chin, Kim On
author_facet Chai, Soo See
Cheah, Whye Lian
Goh, Kok Luong
Chang, Yee Hui Robin
Sim, Kwan Yong
Chin, Kim On
author_sort Chai, Soo See
collection PubMed
description This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weight-to-height ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65, F-score of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is well-generalized. Using Bayes' Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes' Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening high-risk adolescents of developing hypertension.
format Online
Article
Text
id pubmed-8670914
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-86709142021-12-15 A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia Chai, Soo See Cheah, Whye Lian Goh, Kok Luong Chang, Yee Hui Robin Sim, Kwan Yong Chin, Kim On Comput Math Methods Med Research Article This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weight-to-height ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65, F-score of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is well-generalized. Using Bayes' Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes' Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening high-risk adolescents of developing hypertension. Hindawi 2021-12-07 /pmc/articles/PMC8670914/ /pubmed/34917164 http://dx.doi.org/10.1155/2021/2794888 Text en Copyright © 2021 Soo See Chai et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chai, Soo See
Cheah, Whye Lian
Goh, Kok Luong
Chang, Yee Hui Robin
Sim, Kwan Yong
Chin, Kim On
A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia
title A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia
title_full A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia
title_fullStr A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia
title_full_unstemmed A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia
title_short A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia
title_sort multilayer perceptron neural network model to classify hypertension in adolescents using anthropometric measurements: a cross-sectional study in sarawak, malaysia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8670914/
https://www.ncbi.nlm.nih.gov/pubmed/34917164
http://dx.doi.org/10.1155/2021/2794888
work_keys_str_mv AT chaisoosee amultilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT cheahwhyelian amultilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT gohkokluong amultilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT changyeehuirobin amultilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT simkwanyong amultilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT chinkimon amultilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT chaisoosee multilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT cheahwhyelian multilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT gohkokluong multilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT changyeehuirobin multilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT simkwanyong multilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia
AT chinkimon multilayerperceptronneuralnetworkmodeltoclassifyhypertensioninadolescentsusinganthropometricmeasurementsacrosssectionalstudyinsarawakmalaysia