## 4 Materials and Methods
### Development of Machine Learning-Based Models
#### 4.5.2 Machine Learning Techniques  
The ML methods, namely, Naive Bayes (NB), Generalized Linear Model (GLM), Linear Regression (LR), First Large Margin (FLM), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), Gradient Boosted Tree (GBT), and Support Vector Machine (SVM) were implemented in our study using the Rapidminer software [68]. The selection of the nine ML models was made as per the recommendation of the Auto Model feature of the Rapidminer software [86]. Evaluation of the performance of the developed models using validation techniques plays an important role in establishing their generalizability. Cross-validation provides an efficient way of evaluating the performance of ML models with a limited data set. The efficacy of the developed models was examined through a 10-fold cross-validation technique. The partition of the data set into ten equal subsets was performed using a stratified sampling method to implement the 10-fold cross-validation method [75]. In this process, the total data set was divided into training and validation data sub-sets in a 9:1 ratio, randomly 10 times. Finally, the performance of the 10-fold cross-validated ML models was highlighted using the following metrics: accuracy, area under the receiver operating characteristics curve (AUC), precision, sensitivity, F-Measure, and specificity (Table 9). \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Method** & **Description** & **Rapidminer Implementation** & **Ref. ** \\ \hline \multirow{8}{*}{Kernel PCA} & Kernel PCA refers to the generalization of the PCA method using kernels (such as linear, polynomial, or Gaussian) for application to nonlinear data. In this method, the input signal is transformed into a new parameter space using a nonlinear transformation. A kernel matrix \(K\) is formed through the dot product of the newly generated parameters in the transformed space, which act as the covariance matrix. In the end, PCA is performed on the kernel matrix \(K\) and is called the kernel PCA method. & The Kernel PCA method is implemented using the “Weight by PCA (Kernel)” operator. It has two critical attributes, namely kernel type and chosen: & The Kernel PCA method is implemented using the “Weight by PCA (Kernel)” operator. It has two critical attributes, namely kernel type and chosen: & The Kernel)” operator. It has two critical attributes, namely kernel type and chosen: & \\ \cline{1-1}  & ICA can be regarded as a statistical method that decomposes a complex signal into independent components. ICA is a generalization of the PCA method. Nevertheless, ICA optimizes the higher-order statistics (e.g., kurtosis), unlike PCA, which optimizes the 2 nd order statistics of the given signal. Therefore ICA produces independent components, whereas PCA generates uncorrelated components. & The ICA method is implemented using an “Independent Component Analysis” operator. The critical attributes of this operator and their values in our implementation are listed below: & The ICA method is implemented using an “Independent Component Analysis” operator. The critical attributes of this operator and their values in our implementation are listed below: & The ICA method is implemented using an “Independent Component Analysis” operator. The critical attributes of this operator and their values in our implementation are listed below: & The ICA method is implemented using an “Independent Component Analysis” operator. The critical attributes of this operator and their values in our implementation are listed below: & \\ \cline{1-1}  & SVD is also an extension of the PCA method where the highly correlated parameters are eliminated to reduce redundancy. It results in fewer components as compared to PCA, but retains most of the variance of the extracted signal parameters. & The SVD method is implemented using “Self-Organizing Map” operator. The critical attributes of this operator and their values in our implementation are listed below: & The SVD method is implemented using the “Singular Value Decomposition” operator.