## 3 Implementation and Results
### Simulation Results  
We conducted a comparative study of every algorithm utilized in this study. The study's objective was met by creating ML and DL algorithms to recognize Kyphosis disease. Using ML/DL models with learning algorithms; stratified K-fold cross-validation with Hyperparameter tuning has been investigated. Each test underwent 5- and 10-fold cross-validations. The most widely accepted K-fold cross-validation management is often the 5- or 10-fold control.  
As indicated in Table 3 and represented in Figure 14, the LR model reached 81.47% accuracy with an SD of 0.0398 by undertaking 5-fold stratified cross-validation, followed by the NB model at 81.47% accuracy with an SD of 0.0398, the RF model at 85.22% accuracy with an SD of 0.0485, the SVM model at 85.22% accuracy with an SD of 0.0839, the KNN model at 85.22% accuracy with an SD of 0.0740, and the DNN model at 87.72% accuracy with an SD of 0.0666.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**ML/DL** & \begin{tabular}{c} **Classification** \\ **Accuracy in \%** \\ **(with SD)** \\ \end{tabular} & \begin{tabular}{c} **Recall or** \\ **Sensitivity** \\ \end{tabular} & **Specificity** & \
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ **Score** \\ \end{tabular} & **Precision** & **F1-Score** &
\begin{tabular}{c} **AUC-ROC** \\ **Score** \\ \end{tabular} \\ \hline LR & 81.47 (SD = 0.0398) & 0.30 & 0.95 & 0.63 & 0.68 & 0.36 & 0.63 \\ \hline NB & 81.47 (SD = 0.0398) & 0.48 & 0.91 & 0.70 & 0.53 & 0.44 & 0.70 \\ \hline RF & 85.22 (SD = 0.0485) & 0.48 & 0.95 & 0.72 & 0.70 & 0.51 & 0.72 \\ \hline SVM & 85.22 (SD = 0.0839) & 0.48 & 0.95 & 0.72 & 0.68 & 0.51 & 0.72 \\ \hline KNN & 85.22 (SD = 0.0740) & 0.53 & 0.94 & 0.74 & 0.63 & 0.52 & 0.74 \\ \hline DNN & 87.72 (SD = 0.0666) & 0.62 & 0.95 & 0.79 & 0.90 & 0.65 & 0.79 \\ \hline \end{tabular}
\end{table}
Table 3: The comparative performance estimation using different ML/DL models based on the stratified five-fold cross-validation.  
Figure 13: The classification accuracies of the DNN model using the stratified ten-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ML/DL** & \begin{tabular}{c} **Classification** \\ **Accuracy in \%** \\ **(with SD)** \\ \end{tabular} & \begin{tabular}{c} **Recall or** \\ **Sensitivity** \\ \end{tabular} & \begin{tabular}{c} **Specificity** \\ **Specificity** \\ \end{tabular} & \begin{tabular}{c} **Balanced** \\ **Accuracy** \\ **Score** \\ \end{tabular} & \
\begin{tabular}{c} **Precision** \\ **F1-Score** \\ \end{tabular} &
\begin{tabular}{c} **F1-Score** \\ **Score** \\ \end{tabular} \\ \hline LR & 82.64 (SD = 0.0625) & 0.40 & 0.96 & 0.68 & 0.45 & 0.39 & 0.68 \\ \hline NB & 82.64 (SD = 0.0625) & 0.50 & 0.92 & 0.71 & 0.58 & 0.48 & 0.71 \\ \hline RF & 83.89 (SD = 0.0982) & 0.35 & 0.96 & 0.65 & 0.60 & 0.43 & 0.65 \\ \hline SVM & 85.14 (SD = 0.0940) & 0.55 & 0.94 & 0.75 & 0.68 & 0.54 & 0.75 \\ \hline KNN & 84.03 (SD = 0.0535) & 0.50 & 0.94 & 0.72 & 0.55 & 0.46 & 0.72 \\ \hline DNN & 87.64 (SD = 0.0561) & 0.55 & 0.97 & 0.76 & 0.70 & 0.57 & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparative performance estimation using different ML/DL models based on the stratified ten-fold cross-validation.  
Figure 16: The pictorial representation of comparative performance estimation using different ML/DL models based on the stratified five-fold cross-validation.