## 3 Materials and Methods  
ML algorithms can be classified into four subclasses as follows: (1) supervised learning, where the algorithm uses a set of labeled training data to derive a function between the input(s) and the output(s); (2) unsupervised learning, where the algorithm does not require labeled data; (3) semi-supervised learning, where the training dataset includes both labeled and unlabeled data, and (4) reinforcement learning, where the algorithm rewards positive actions and penalizes negative actions using a feedback mechanism [33]. Although the availability and quality of data are critical to the performance of ML algorithms, supervised learning usually achieves better results than semi-supervised and unsupervised learning approaches [33]. A DT is a supervised ML model that supports decision-making processes. It is represented as a logical tree, with a range of conditions and conclusions as nodes and branches [38; 39]. At each stage of the decision-making process, a decision tree takes a decision and considers alternative choices of action, selecting the most relevant alternatives [40]. It employs estimates and probabilities to calculate probable outcomes and assist in determining the net gain from a decision. Various techniques are employed to accomplish classification, including information gain, chi-square, Gini, entropy, and others [41]. The DT method is characterized by its simplicity, ease of understanding and visualization, rapid execution, minimal data preprocessing requirements, and capacity to effectively handle both categorical and numerical data; however, it should be noted that this algorithm can occasionally result in a complex tree structure that lacks sufficient generalization, and it is a relatively unstable model [42]. The RF model can be considered an ensemble learning method for regression and classification [43]. The RF model builds an ensemble of DTs using a training sample, which, in turn, results in the appropriate classification of data or the provision of suitable predictors for one or more characteristics of the individual trees. The RF method is employed in the construction of a bagging ensemble, with the process being informed by DTs and introducing random attribute selection during the training process [44]. The RF algorithm dividesthe training set into multiple subsets, and each subset is then assigned an independent DT [45]. The model's output is determined by a voting system that integrates the outputs from all the DTs [46]. In contradistinction to alternative conventional methods, which necessitate a substantial computational load, the RF model entails a reduced computational demand for the DT and enhances prediction accuracy [47]. [PERSON] et al. [48] demonstrated that a single DT is sensitive to the noise present within the training dataset. However, the RF classifier has been shown to be capable of reducing the correlation between the trained multiple DTs, thereby effectively reducing the overfitting problem. The logistic regression (LR) model aims to construct a linear model of the output, which can be interpreted as an estimate of the posteriori probability of class membership [49]. LR is a widely used classification algorithm in industry and because of its simplicity and efficiency, LR still attracts wide attention from researchers [50]. LR is a statistical model with an assumption that the variables are independently and identically distributed. LR is one of the wrapper methods known for its decent predictability performance but is computationally expensive [51]. LR does not require the optimization of any hyperparameter and is thus easier to implement [52]. Based on the observation of input variables, LR describes the probability of realization of the output variable(s). LR applies the Newton-Raphson algorithm to determine the coefficients of the regression equation [53]. The SVM is an algorithm that is capable of handling both regression and classification tasks, although they are more effective for classification [42].