## 4 Discussion  
Machine learning techniques are increasingly being used to predict hypertension. However, the models' comparability and effectiveness in real-world applications have been hampered by the incorporation of diverse features and learning techniques. In this work, we examined the possibility of employing simple anthropometric factors to predict hypertension. Additionally, we examined the effects of various machine learning approaches on this prediction using only basic anthropometric data. The use of simple anthropometric measures promises a simple, straightforward, affordable, and practical technique of predicting hypertension, particularly when a blood pressure monitor is not accessible. We defer the use of additional physiological data, such as blood pressure and heart rate, as well as self-reported lifestyle parameters, for the sake of simplicity and objective input features. In terms of machine learning models, none of the 13 ML models scored well across all seven performance metrics: accuracy, precision, sensitivity, specificity, \(F1\)-score, misclassification rate, and AUC. Except for sensitivity, LightGBM, Random Forest, CatBoost, and XGBoost are the four leading models in the six performance criteria. The performance rankings of these four models, on the other hand, are inconsistent across these six performance measures. On the other hand, the Decision Tree has the lowest performance across all performance measures. In this study, we investigated three different types of supervised learning algorithms: neural networks, ensemble models, and classical models. The models in each of these categories are listed in Table 9. The kNN model beat the other classical models in terms of AUC and \(F1\)-score but lagged behind the Naive Bayes model in terms of accuracy, precision, specificity, and misclassification rate. The Logistic Regression model performed the best in terms of sensitivity. While ensemble models are known to produce more accurate results than classical models, their performances for certain types of these models are not as good as classical models. AdaBoost, for example, falls short of kNN in terms of AUC. On the other hand, the MLP model performs modestly across the board, although it is an interesting model for sensitivity. The results revealed that each model outperformed the others in terms of the numerous performance metrics used. Because of this, selecting the most appropriate model for practical application might be challenging. 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):  
\[\mathrm{P(A|B)}=\frac{\mathrm{P(A)}\times\mathrm{P(B|A)}}{\mathrm{P(B)}} \tag{7}\]  
P(A): prevalence of adolescent hypertension in Sarawak population = 0.301  
P(B): probability of the model returning positive  
P(B 1 A): probability of event B given event A occurring  
Table 10 presents a summary of the findings acquired through the use of Bayes' Theorem. From this table, the top three performing ML models are LightGBM (0.5799), Random Forest (0.5542), and XGBoost (0.5397), whereas the bottom three are Decision Tree (0.3788), SVM (0.4471), and LogitBoost (0.4649). With the highest performing model, LightGBM predicts an adolescent in the Sarawak adolescent population of 200,130, with a \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Model** \\ \hline Neural Network & Multi-layer Perceptron (MLP) \\ Ensemble Model & LogitBoost, AdaBoost, Gradient Boosting, Random Forest, XGBoost, CatBoost, LightGBM \\ Classical Model & SVM, Decision Tree, Logistic Regression, Naive Bayes, kNN \\ \hline \end{tabular}
\end{table} 
Table 9: Different learning models in the three different supervised machine learning categories.