## 3 Materials and Methods
### Classification Methods  
Support vector machine (SVM) is a widely used machine learning method for classification problems; because of its flexibility and simplicity properties, that are used in addressing classification problems, the SVM method provides a characteristic and balanced predictive performance that is needed to perform well during studies with potentially small sample sizes. It also has the potential to help solve big data classification problems. Therefore, the SVM method has proven to be an efficient and flexible tool for classification and analysis [21]. SVM is a type of supervised machine learning algorithm classifier that is utilized for the examination of data that is also involved in the identification of various patterns which are used for the classification. Between a series of patterns of various classes, this classifier creates a hyperplane. The SVM linear classifier's mathematical form is displayed in (1). \[\text{f(x)}=\text{WT}\,\text{X}\,+\,\text{b} \tag{1}\]  
Random forest (RF) is a classification and regression technique which depends on the aggregation of several decision trees. RFs are used to analyze and classify quantities of data. Specifically, it is a set of trees used to build a training dataset and conduct an internal evaluation in order to arrive at a reasonable prediction of the response given future predictions. This is undertaken so that the model can be used to make predictions [22]. Logistic regression (LR) analysis can be defined as one of the statistical methods that are based on evaluating the relationship between all different prediction variables, whether they are definitive or continuous. When there is more than one variable, LR is used to obtain the probability ratio. LR works similarly to linear regression to a large extent, but the difference between them is that logistic regression works with a variable whose response is binomial. To reduce all the effects of complex factors, LR analyzes all multiple variables simultaneously, so the LR algorithm is a very powerful tool [23]. The decision tree's (DT) structure is the same as that of a regular tree, which consists of roots, branches, and leaves in the internal nodes where the attribute is tested. In the branches, the test result is in it, and in the leaf node, the test result is labelled. DT algorithms use attributes and segmentation to test which nodes are better at segmenting into individual classes. The training data are divided into subgroups according to the values of the segmentation attributes. The algorithm continues until all the similarities are in a group of the same class [24]. K-nearest neighbor (KNN) is one of the most straightforward ML methods for solving classification issues. The notion behind how the algorithm is supposed to function is that items that are close to each other also have attributes that are close to and comparable toeach other. The demand for a considerable amount of memory to store the entire sample is one of the primary drawbacks of using KNN classifiers. When the sample size is huge, the reaction time on a sequential computer likewise increases significantly. Despite this issue, it still performs satisfactorily. Classifiers benefit from their improved performance because it helps them solve classification difficulties [25]. Artificial neural networks (ANNs) are utilized in dataset analysis and classification, while they can also be involved in the identification of recognized patterns and make predictions. When available, ANNs can analyze massive data and produce very precise predictions. The most common and used ANNs are connected and also supervised networks [26]. The Bayes theorem is the basis for the Naive Bayes (NB) classification method, which is a simple probabilistic classification approach. It also operates on the presumption that each feature is distinct from the others. The model must first be instructed using the training dataset in order to produce reliable predictions using the predict function.