## 4 Materials and Methods
### Development of Machine Learning-Based Models
#### 4.5.1 Selection of Input Parameters  
The selection of suitable input parameters is important for the proper performance of the machine learning models [69]. In this study, the choice of the parameters was performed using the parameter ranking methods of (Information Gain (IG), Information Gain Ratio (IGR), Uncertainty, Gini Index (GI), Chi-Squared Statistic (CSS), Correlation, Deviation, Relief, Rule, Tree Importance (TI), Support Vector Machine (SVM), and Component Model (CM)), and the dimensionality reduction methods of (Principal Component Analysis (PCA), Kernel PCA, Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and Self-Organizing Map (SOM)) [40]. A short description of the parameter selection methods and their Rapidminer implementation details are provided in Table 8. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Description** & **Rapidminer Implementation** & **Ref.** \\ \hline \multirow{6}{*}{IG} & IG is a weight-based parameter selection method that & The IG method is implemented using the “Weight by Information Gain” \\  & describes the dependency between a parameter \(X\) & operator. It finds out the relevance of the parameters through \\  & with a class variable \(Y\) by dividing information & information gain and allocates weights to them. It has three critical \\  & about the reduction in entropy. IG is defined by & attributes, namely, normalize weights, sort weights, and sort direction. \\  & Equation (14) [70]. & In our study, the following values of the attributes were chosen: \\  & \(IG=H(Y)-(Y\backslash X)=H(X)-H(X\backslash Y)\) & (14) & normalize weight = false \\  & where \(H(Y)\) represents the entropy of \(Y\) and \(H(Y\backslash X)\) & 2. & sort weight = true \\  & corresponds to the entropy of \(Y\) given \(X\). & 3. & sort direction = ascending \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Description of the parameter selection methods. \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Method** & **Description** & & **Rapidminer Implementation** & **Ref. ** \\ \hline IGR & IGR is a parameter selection method that is derived from IG by dividing it with the entropy of the parameter (Equation (15)). IGR helps to expiate the bias introduced by the IG method. & The IGR method is implemented using the “Weight by Information Gain Ratio” operator. It detects the relevance of the parameters through the information gain ratio and allocates weights to them. It has three critical attributes, namely, normalize weights, sort weights, and sort direction. & \\  & \(IGR=\frac{\mathrm{IG}}{H(X)}\) & (15) & 1. normalize weight = false & \\  & where \(\mathrm{H}(X)\) represents the entropy of parameter X. & 3. & sort direction = ascending & \\ \hline Uncertainty & Uncertainty is another parameter selection method that focuses on eliminating the inherent bias introduced by the IG method. It is computed as the ratio of twice the IG to the sum of the entropies of the parameter X and the class variable Y (Equation (16)). & The uncertainty method is implemented using the “Weight by Uncertainty” operator. The uncertainty method measures the symmetrical uncertainty to identify the importance of the parameters. It has four critical attributes, namely, normalize weights, sort weights, sort directions, and number of bins. In our study, the following values of the attributes were chosen: & \\  & \(U=\frac{2\times 3\times 6}{\mathrm{IG}(X)}\) & (16) & 1. normalize weight = false & \\  & where U indicates the uncertainty of Y. & 3. & sort direction = ascending & \\ \hline GI is an impurity-based parameter selection method. It describes the likelihood of the wrong classification of a randomly selected variable. For a given data set \(\mathrm{S}\) (\(\mathrm{e}_{x}\), \(\mathrm{s}_{1}\), \(\mathrm{s}_{2}\), \(\mathrm{s}_{3}\), \(\mathrm{s}_{4}\)) and a class variable \(\mathrm{C}_{1}\) (\(\mathrm{C}_{1}\) \(\mathrm{s}_{2}\) \(\mathrm{s}_{3}\)), GI is computed using Equation (17).