## 2 Materials and Methods
### Artificial Neural Networks  
The human brain is an incredibly intricate, non-linear, parallel computer (processor). It has structural components called neurons to carry out several tasks such as perception, identification of patterns, and motor control [56]. There are an estimated 1011 neurons in the brain, and these neurons are connected to each other with an estimated 1015 connections [57]. ANN can be expressed as the summary of the computational model of the brain [58]. ANNs are a subset of artificial intelligence that aims to replicate how the brain processes and stores information. It is a network structure that functions by establishingconnections between neurons, which are units of mathematical processing [59]. Figure 2 shows the artificial neuron structure. There are many definitions and approaches to ANNs, but they are distributed processors composed of neurons [58]. In ANNs, information is obtained from the network through a learning process. Connections between neurons are called synaptic weights, and they are utilised to store newly learned information [56]. In ANNs, learning takes place with the system called the backpropagation algorithm. Learning is actually a matter of determining synaptic weights. One of the supervised learning models, the ANN, uses input-output pairs as its training set. An ANN searches for a function that, given inputs, generates outputs. Applying training data repeatedly over time the network approximates a function for that input space [59]. When the studies in the literature are examined, there are different ANN models such as MLP, SOM, RBF, CNN, RNN, LSTM, LVQ, and LAM [60; 61; 62; 63; 64; 65]. In this study, feed-forward ANN, which is the most preferred and frequently used in the literature, was used. In the MLR analysis, the predictor variables specified in Figure 2 were included in the analysis. However, in order for the model established in MLR analysis to be reliable and usable, it must pass multicollinearity, heteroscedasticity, linearity, and normality diagnostic tests [55]. High correlation leads to a multicollinearity problem. To detect this problem, the VIF value among the predictor variables must be examined. A VIF < 10 indicates that there is no multicollinearity in the model [66]. As each VIF value is lower than 10 (all VIFs < 10), the model does not have a multicollinearity issue. For heteroskedasticity detection, Breusch-Pagan/Cook-Weiseberg heteroskedasticity detection was performed [67]. The result is \(p\) > 0.05. According to this result, there is no heteroscedasticity in the model. To determine linearity, the Augmented Component Plus Residual graph of the predictor variables in the model was drawn [55]. It was observed that the predictor variables were linearly related to the predicted variable. One of the most important steps of MLR is that the errors of the estimated model come from a population that is normally distributed. We performed a Shapiro-Wilk W test to check whether the errors were normally distributed or not [54]. Since the result is \(p\) > 0.05, there is no normality problem in the model. Figure 3 displays this study's flow chart. Figure 2: Illustration of an Artificial Neuron [56]. ML is actually a step in the process of discovering the information contained in databases. Different methodologies such as SEMMA, CRISP-DM, and KDD are followed when discovering knowledge from data. Every methodology's aim is to reveal the information that is hidden in the data. Many steps are similar and common in these methodologies, one of which is the ML stage. Following the purpose of our study, the steps shown in Figure 3 were processed sequentially. As a first step, data that were suitable for our purpose were selected from the data in the database. Secondly, the data preprocessing phase was started. At this stage, descriptive statistics of the data included in the analysis were examined. At this stage, it was also checked whether there was any missing data in the data set, and the presence of outliers was investigated with extreme value analysis.