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

Abstract

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.

Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases

“…We can state that the machine learning model, MLP with 20 hidden layers, can be utilized to forecast efficiently the future death cases of COVID-19 in Pakistan. Our MLP model performance is similar to that of Srinivasa and Santhi Thilagam [ 37 ], Deyasi et al [ 38 ], and Chai et al [ 39 ].…”

“…Figure 1 shows the fowchart for this methodology. [37][38][39] is acknowledged as one of the most fexible mathematical algorithms according to its potential applications as well as its precision in time series predicting and forecasting. Te MLP model is particularly useful in approximating any type of continuous, nonlinear, diferentiable, and limited function.…”

Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well?

“…For the purpose of selecting a realistic model, we propose that Bayes' Theorem be used to verify the model's applicability before selecting it. Using Bayes' Theorem, we can determine how well a model performs in a particular population when the prevalence of a specific condition is taken into consideration [19]. Bayes' Theorem is a calculation of the posterior probability based on the mathematical formula shown in (7):…”

“…Therefore, our study intends to fill this research gap. In our previous work [19], we used anthropometric measurements and simple demographic data to develop a one hidden layer of 50 neurons Multilayer Perceptron (MLP) neural network to predict hypertension in adolescents, yielding a sensitivity of 0.41, specificity of 0.91, precision of 0.65, F1-score of 0.50, accuracy of 0.76, and AUC of 0.75. In this study, we extend our previous work by investigating the efficacy of thirteen different ML models: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, k-Nearest Neighbor, Multilayer Perceptron, Gradient Boosting, XGBoost, LightGBM, CatBoost, AdaBoost, and LogitBoost, from the three supervised ML categories of neural network, ensemble model and classical model for hypertension prediction in adolescents using anthropometric measurements and simple demographic data.…”

Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods

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