## 4 Results
It changes the rate at which neuron weights are adjusted per learning step and is essential for the performance of a neural network. While the consensus is that low learning rates slow learning down, high learning rates might keep the network from converging. This study utilised J48, SVM, RF, rotation forest, and ANN for training with various hyperparameters. The ranges of the hyperparameters are presented in Table 3.  
Usually, machine learning models split data sets into training and testing sets. Training is used to train the model while testing sets are used to test the model. Various approaches such as k-fold cross-validation and train test Split are used to validate results [8; 35; 43]. In the train test split, values are set for the model on how much data the model has to train and test. Mostly, it performs well for large datasets. In this research, the 10-fold cross-validation \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & & **Hyperparameters** & \\ \hline J48 & Confidence factor [0.05–0.50] &
\begin{tabular}{c} Minimum number of \\ instances per leaf nodes [2; 3; 4; 5; 6] \\ \end{tabular} & Random seed [1] \\ SVM & Kernel type [1; 2; 3] & Epsilon [1.0 \(\times\) 10\({}^{-12}\)] & Random seed [1] \\ RF & Number of trees [50; 100] & Maximum depth of trees [15] & Random seed [7; 8; 9; 10; 11] \\ Rotation Forest & Ensemble size [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15] & Maximum depth of trees [15] & Random seed [1] \\ ANN & Learning rate [0.3] & Number of hidden layers [2] & Random seed [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The ranges of the hyperparameters of the classifiers. technique was utilised to obtain effective results on small datasets. The cross-validation approach works, as it splits the dataset into three portions to train, test, and validate the set. K sets the value to guide the model regarding how many equal folds of datasets to prepare after division. The first fold was used for testing purposes, the remaining \(k-1\) folds were used to train the model, and the whole process was repeated k times. In this study, various machine learning models with feature selection methods were used. The stopping criteria for feature selection methods was set so that when the performance of the models decreased, the execution of feature selection methods stopped. The fourteen most significant features, as selected through feature selection methods, were used. The J48 algorithm derived results by using the approach of post-pruning. Post pruning is the process of evaluating decision tree error at each decision tree junction. The pruning of decision trees optimises the computational efficiency of the model. The pruning method reduces the size of the tree and unnecessary complexity. To test the effectiveness of post-pruning, the hyperparameter is often labelled as a confidence factor. If the value of the confidence factor is kept low, then the amount of post-pruning is decreased. Moreover, the minimum instances per leaf node are set, which means to set the minimum amount of separation. It guarantees that at least two of the branches have the minimum number of instances at each split. For example, if one instance is separated from 100 instances, it does not give much information. The J48 decision tree model was combined with feature selection methods. The best results were obtained using the relief-F-based feature selection technique, which achieved maximum accuracy of 68.5% with an ROC value of 0.63 when the model has was with a confidence factor of 0.50. The minimum number of instances per leaf node was 6. The complete results for the J48 classifier with feature selection methods are presented in Table 4. After the J48 decision tree, SVM was utilised to obtain more effective results. In SVM, the kernel type was selected. Hence, the kernel type, i.e., PolyKernel, Normalized PolyKernel, and radial basis function (RBF) Kernel, were chosen for better performance.