## 2 Materials and Methods
### Methodology Design and Implementation
#### 2.2.4 Machine Learning Models  
We developed and tuned thirteen ML models of three categories: neural network (Multilayer Perceptron), classical model (Logistic Regression, Decision Tree, Naive Bayes, k-Nearest Neighbor), and ensemble model (Random Forest, Support Vector Machine, Gradient Boosting, XGBoost, LightGBM, CatBoost, AdaBoost and LogitBoost) to predict hypertension. Python was used throughout the implementation. The hyperparameters of the models were established during training using grid search and 10-fold cross-validation on resampled data. * Logistic Regression  
This is the most basic and widely used machine learning model for binary classification, which may easily be extended to multi-label classification problems. The Logistic Regression technique utilizes the sigmoid function to construct a regression model that predicts the chance that an input belongs to a particular category. * Decision Tree  
This technique repeatedly separates the dataset according to an optimal data separation criterion, resulting in a tree-like structure [36]. The most popular splitting criteria used include the Information Gain, Gini index and Gain ratio. Splitting aims to create pure nodes, i.e., to reduce the impurity of a node. * Random Forest  
Random Forest is a Decision Tree-based ensemble learning model composed of multiple Decision Trees. Each Decision Tree in the Random Forest will eventually yield a leaf node. The Random Forest makes predictions based on the output chosen by the majority of Decision Tree leaf nodes. * Support Vector Machine (SVM)  
The SVM method classifies data by building a multidimensional hyperplane that best separates two classes by finding the maximum margin between two data clusters. This approach achieves a high level of discrimination by changing the input space into a multidimensional space through the use of unique nonlinear functions referred to as kernels. * Naive Bayes  
Naive Bayes is a Bayes' Theorem-based probabilistic algorithm. It asserts that the presence of one feature in a class does not influence the presence of any other features. * k-Nearest Neighbor (kNN)  
A kNN algorithm assumes that similar objects are close to each other. The similarity is expressed in kNN by determining the distance between two points on a graph. The classification of a data point is based on the majority vote of its k-Nearest Neighbor in a distance function. * Multilayer Perceptron (MLP) An MLP is a feedforward artificial neural network (ANN) that has input, hidden, and output layers. The input layer accepts signals, whereas the output layer classifies or predicts them. There is an arbitrary number of hidden layers between the input and output layers. These hidden layers are the MLP's true computational engine. The non-linear activation function is used in the hidden layers and the output layer. The backpropagation procedure is used to train MLP models. * Gradient Boosting This is an ensemble learning model that makes predictions by combining many weak learning models, often Decision Trees. Gradient Boosting works by iteratively reducing the loss function by selecting a function that points to a negative gradient, i.e., a weak hypothesis. * XGBoost XGBoost is the abbreviation for eXtreme Gradient Boosting developed by [PERSON] et al. [37]. It is a Decision Tree-based ensemble machine learning model that leverages the Gradient Boosting framework. Using parallel boosting, a new Decision Tree model is added to compensate for the weaknesses of the previous model. It is designed for speed and performance. * LightGBM LightGBM, or Light Gradient Boosting Machine, is a Gradient Boosting framework comparable to XGBoost that employs Decision Tree learning approaches. Microsoft designed it in 2017 [38] for increased speed. The attributes are sorted and categorized into bins using a histogram-based Decision Tree learning method, and the leaves are grown leaf-wise, yielding improved efficiency and memory usage advantages over XGBoost.