###### Abstract  
The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. It is essential to assess the impacts of the chosen machine learning models using simple anthropometric measurements. We developed and tested 13 machine learning methods of neural network, ensemble, and classical categories to predict hypertension in adolescents using only simple anthropometric measurements. The imbalanced dataset of 2461 samples with 30.1% hypertension subjects was first partitioned into 90% for training and 10% for validation. The training dataset was reduced to eight simple anthropometric measurements: age, C index, ethnicity, gender, height, location, parental hypertension, and waist circumference using correlation coefficient. The Synthetic Minority Oversampling Technique (SMOTE) combined with random under-sampling was used to balance the dataset. The models with optimal hyperparameters were assessed using accuracy, precision, sensitivity, specificity, \(F1\)-score, misclassification rate, and AUC on the testing dataset. Across all seven performance measures, no model consistently outperformed the others. LightGBM was the best model for all six performance metrics, except sensitivity, whereas Decision Tree was the worst. We proposed using Bayes' Theorem to assess the models' applicability in the Sarawak adolescent population, resulting in the top four models being LightGBM, Random Forest, XGBoost, and CatBoost, and the bottom four models being Logistic Regression, LogitBoost, SVM, and Decision Tree. This study demonstrates that the choice of machine learning models has an effect on the prediction outcomes. adolescents; anthropometric; hypertension; imbalanced dataset; machine learning prediction; SMOTE +
Footnote †: journal: Applied Sciences  
0000-0001-9993-3227]S. S.  
account for 17.9 million deaths per year, are from cardiovascular disease. Hypertension is a crucial factor in the development of cardiovascular disease. Hypertension, often known as high blood pressure, is defined as a systolic blood pressure reading \(\geq\)140 mmHg and/or a diastolic blood pressure reading \(\geq\)90 mmHg. Systolic blood pressure measurements show the pressure in the blood vessels when the heart beats or contracts, whereas diastolic blood pressure measurements represent the pressure in the blood vessels when the heart rests in between beats. A recent study [2] reported that every 20 mmHg systolic and 10 mmHg diastolic pressure increase above a baseline blood pressure of 115/75 doubles the risk of cardiovascular death. Hypertension is no longer only an \"adult disease\"; a growing number of teenagers and younger children are succumbing to it [3,4], as a result of today's youth's physically inactive lifestyle. There is growing evidence that childhood hypertension is a precursor to adult hypertension [5]. Unfortunately, children's hypertension is not diagnosed until it has progressed to the point of being life-threatening or until they reach adulthood [6]. Given the long-term health consequences of uncontrolled hypertension, as well as the fact that pediatric (age 2 to 18) hypertension is a diagnostic signal for numerous important underlying medical illnesses, the need for early and correct diagnosis cannot be overstated. Furthermore, before any further clinical indications arise, childhood and adolescence are the important stages for effective treatment and prevention of hypertension-related cardiovascular problems. Malaysia, a middle-income country, has a prevalence of 24.5% for hypertension in adolescents [7].