Table 7.

Summary of machine learning methods utilized in this work.

Machine Learning	Explanation	Advantage
AdaBoost	An ensemble learning algorithm that combines the predictions of multiple weak learners to produce a single, strong learner	Can handle imbalanced data
Bagging	Ensemble learning algorithm that works by creating multiple bootstrap samples of the training data and training a separate model on each sample	Can improve model with imbalanced data
BernoulliNB	Naive Bayes classifier that is specifically designed for binary classification problems	A simple but effective algorithm that is often used for imbalanced classification tasks
Decision Tree	Decision trees are a type of machine learning model that learns to classify data by constructing a tree of decision rules	Decision trees are relatively robust to imbalanced data, but they can be prone to overfitting
Extra Trees	Ensemble learning algorithm that is similar to random forests, but it uses a different approach to bootstrap sampling	Often used for imbalanced classification tasks because they are less likely to overfit than random forests
Gradient Boosting	An ensemble learning algorithm that combines the predictions of multiple weak learners in a sequential manner	A powerful algorithm that can be used for a variety of machine learning tasks, including imbalanced classification
K-nearest neighbors	K-nearest neighbors (KNN) is a simple but effective machine learning algorithm that classifies data by finding the K most similar training examples to a new data point and predicting the class of the new data point based on the classes of the K most similar training examples	Easy to implement and small dataset can use for imbalanced classification task
Linear Discriminant Analysis	Machine learning algorithm that projects the data onto a lower-dimensional space in a way that maximizes the discrimination between the different classes	Good choice for imbalanced data because it is able to find the most important features for discriminating between the classes
Logistic Regression	A machine learning algorithm that is used for binary classification problems	Often used for imbalanced classification task
Multilayer Perceptron	Multilayer perceptrons (MLPs) are a type of artificial neural network	Can use for classification task and able to learn complex data, even imbalanced classification task
Random Forest	Random forests are an ensemble learning algorithm that combines the predictions of multiple decision trees to produce a single prediction	Ability to handle imbalanced data and their resistance to overfitting
Support Vector Machine:	Machine learning that can use as classification and regression	Can handle high-dimensional data and imbalanced data