Key result
A multilayer perceptron neural network model using simple anthropometric and sociodemographic data classified adolescent hypertension with an accuracy of 0.76 and an AUC of 0.75.
Why the study?
Does a multilayer perceptron neural network model using anthropometric and sociodemographic data accurately classify hypertension in adolescents?
Cross-Sectional (n=2,461)
Does a multilayer perceptron neural network model using anthropometric and sociodemographic data accurately classify hypertension in adolescents?
A neural network model using simple anthropometric and sociodemographic data can serve as a preliminary screening tool for adolescent hypertension, though sensitivity remains modest.
May support MLP-based adolescent hypertension screening from routine data; leaves open prospective validation before clinical use.
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, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>F</a:mi> </a:math> -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.
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Chai et al. (2021) conducted a cross-sectional in adolescent hypertension (n=2,461). Multilayer perceptron (MLP) neural network model was evaluated on Hypertension classification performance (sensitivity, specificity, precision, F-score, accuracy, AUC) (AUC 0.75). A multilayer perceptron neural network model using simple anthropometric and sociodemographic data classified adolescent hypertension with an accuracy of 0.76 and an AUC of 0.75.
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