## 4 Results and Discussion  
Based on LR, NB, RF, KNN, SVM, and DNN models, the effects of Hyperparameter tuning on the dataset for Kyphosis disease have been thoroughly examined. The ML/DL model's performance was maximized using that set of Hyperparameters. We compared these models using several metrics, including accuracy, recall or sensitivity, specificity, and precision. We also considered F1 and AUC-ROC scores. According to our findings, the LR model used 5-fold cross-validation to obtain 81.47% accuracy with an SD of 0.0398, a recall value of 0.30, a specificity of 0.95, a balanced accuracy score of 0.63, a precision of 0.68, an F1-score of 0.36, and an AUC-ROC score of 0.63. With a 0.48 recall value, 0.91 specificities, a 0.70 balanced accuracy score, 0.53 precision, a 0.44 F1-score, and 0.70 AUC-ROC scores, the NB model had an accuracy rate of 81.47% with an SD of 0.0398. With a recall value of 0.48, a specificity of 0.95, a balanced accuracy score of 0.72, a precision of 0.70, an F1-score of 0.51, and an AUC-ROC score of 0.72, the RF model had an accuracy rate of 85.22% with an SD of 0.0485. With a 0.48 recall value, 0.95 specificities, a 0.72 balanced accuracy score, 0.68 precision, a 0.51 F1-score, and a 0.72 AUC-ROC score, the SVM model had an accuracy rate of 85.22% with an SD of 0.0839. With a 0.53 recall value, 0.94 specificities, a 0.74 balanced accuracy score, 0.63 precision, a 0.52 F1-score, and a 0.74 AUC-ROC score, the KNN model had an accuracy rate of 85.22% with an SD of 0.0740. On the other hand, the DNN model achieved 87.72% accuracy with an SD of 0.0666, a 0.62 recall value, 0.95 specificities, a 0.79 balanced accuracy score, 0.90 precision, a 0.65 F1-score, and 0.79 AUC-ROC scores. Similarly, with 10-fold cross-validation, the LR model achieved 82.64% accuracy with an SD of 0.0625, a 0.40 recall value, 0.96 specificities, a 0.68 balanced accuracy score, 0.45 precision, a 0.39 F1-score, and 0.68 AUC-ROC scores. With a 0.50 recall value, 0.92 specificities, a 0.71 balanced accuracy score, 0.58 precision, a 0.48 F1-score, and a 0.71 AUC-ROC score, the RF model had an accuracy rate of 83.89% with an SD of 0.0982. With a 0.35 recall  
Figure 17: The pictorial representation of comparative performance estimation using different ML/DL models based on the stratified ten-fold cross-validation. value, 0.96 specificities, 0.65 balanced accuracy score, 0.60 precision, 0.43 F1-score, and 0.65 AUC-ROC scores, the RF model had an accuracy rate of 83.89% with an SD of 0.0982. With a 0.55 recall value, 0.94 specificities, 0.75 balanced accuracy score, 0.68 precision, 0.54 F1-score, and 0.75 AUC-ROC scores, the SVM model had an accuracy rate of 85.14% with an SD of 0.0940. With a 0.50 recall value, 0.94 specificities, a 0.72 balanced accuracy score, 0.55 precision, a 0.46 F1-score, and 0.72 AUC-ROC score, the KNN model had an accuracy rate of 84.03% with an SD of 0.0535. At the same time, the DNN model had an 87.64% accuracy rate with an SD of 0.0561 and had a 0.55 recall, 0.97 specificities, a 0.76 balanced accuracy score, 0.70 precision, a 0.57 F1-score, and 0.76 AUC-ROC scores. Overall, the Hyperparameter-tuned DNN models excelled over the other models. The DNN model was shown to perform better than all other models in classification accuracy, with accuracy validated by stratified 10-fold and 5-fold cross-validation of 87.64% with SD of 0.0561 and 87.72% with SD of 0.0666, respectively. Regarding classification accuracy, recall or sensitivity, specificity, a balanced accuracy score, precision, an F1 score, and an AUC-ROC score that supported stratified 5- and 10-fold cross-validation, the DNN model outperformed a wide variety of other models, as shown in Tables 3 and 4, respectively. This model likewise received the highest score based on these additional performance metrics. If we compare our results with the previous work reported by others, we can find some significant accuracy improvements in our proposed model.