## 3 Result Analysis and Comparative Discussions
### Diagnostic Performance Analysis  
The best possible feature subset captured by Boruta is given to the k-NN for multiclass-classification in a supervised manner. With the help of Equation (1), the class-based accuracy is calculated. The Equation (1) is counted as the precision (positive predictive value) for machine learning based algorithms for classwise accuracy. Therefore, in this study, the precision score is considered as the main performance parameter to determine the best performance. In this equation, the term \"true positive\" refers to the correctly classified samples from the provided test data to the algorithm. Similarly, the term \"false positive\" refers to the negatively classified samples.  
\[\textit{Class\_based\_accuracy}=\frac{\textit{True\_positive}}{\textit{True\_positive}+\textit{False\_positive}} \tag{1}\]  
Therefore, with the help of Equation (2), the average of classification accuracy is computed.  
\[\textit{Avg\_accuracy}=\frac{\textit{True\_positive}+\textit{True\_negative}}{ \textit{Total\_number\_of\_samples}} \tag{2}\]  
To obtain the final classification accuracy, eight-fold cross-validation is used. The final accuracy is obtained after eight-fold cross-validation. The proposed approach achieves 99.7% classification accuracy when the optimal value of \(k\) is eight in k-NN algorithm (illustrated into Figure 7). Moreover, in Table 3, the performance analysis of the proposed approach is depicted in a very detailed manner. To verify the robustness of the suggested method, besides Equation (1) and (2), the recall (sensitivity) score, and F1 value is also calculated. For recall score, Equation (3) is used, and for F1 value, Equation (4) is utilized. In addition, Figure 8 illustrates the confusion matrix for the proposed approach.  
\[\textit{Recall\_score}=\frac{\textit{True\_positive}}{\textit{True\_positive}+ \textit{False\_negative}} \tag{3}\]  
Figure 6: Boruta feature space for different health conditions.  
(4)  
Along with the proposed approach, several assessments are made to establish the robustness. Besides the proposed method, three approaches are measured for comparative study: (1) reduction of the feature space by using PCA to use the k-NN for final classification, (2) dimensionality reduction of the HBoF by t-SNE and then usage of k-NN for final classification, and (3) taking all the features into account to use the k-NN for final health classification. In Table 4, the classification accuracies from different approaches are portrayed in a comprehensive way. From the information shown there, the necessity of finding out the ranked features is demonstrated, as opposed to keeping all the information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Health Types** & **Class Based Accuracy (\%)** & **Recall Score (\%)** & **F1 Score (\%)** \\ \hline Normal Condition (NC) & 100 & 100 & 100 \\ Faulty Condition 01 (FC1) & 99.50 & 99.75 & 99.62 \\ Faulty Condition 02 (FC2) & 99.74 & 99.50 & 99.62 \\ Average & 99.75 & 99.75 & 99.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance analysis of the proposed method.  
Figure 8: Confusion matrix for the proposed method.  
Figure 7: Various categorization accuracies. The optimal value is \(k=8\).  
From Table 4, it is observable that when there is no feature selection algorithm after extracting all the features (HBof + k-NN), the classifier (k-NN) failed to distinguish among different health types. Then again, when the manifold learning technique--t-SNE and dimensionality reduction technique--PCAs are used as feature selectors, they failed to differentiate among different health types into the low-dimensional feature space. Moreover, both algorithms mainly compress the high dimensional data into low dimension. Unlike the proposed feature selector Boruta, these algorithms do not signify the importance of any individual feature. Thus, the proposed approach signifies the importance of the feature selector--Boruta for multiclass classification in this experiment.