## 4 Discussion
By comparing the ML models as seen from Tables 15 and 16, we observed that, by performing 5- or 10-Fold Cross-Validation based on the RF model, the best parameters did not change much, setting bootstrap to true, criterion to either entropy or gini, and the number of trees to 100, really improved the RF model in both cases as seen from Figures 16 and 17 respectively. Among the SVM classifiers, the SVM-Linear model performed better when both the cost function and gamma had the same value of 1, based on the 5-Fold Cross-Validation, as compared with the 10-Fold Cross-Validation when the value of the cost function was 0.1 and gamma was 1, as shown in Tables 15 and 16 respectively. The SVM-RBF model performed better based on the 10-Fold Cross-Validation when the values of the cost function and the gamma were 10 and 0.1 respectively, as compared with the 5-Fold Cross-Validation when both the cost function and the gamma had the same value of 1, as seen from Table 15. For the SVM-Poly model, the model gave the same accuracy in both cases, based on the 5- and 10- Fold Cross-validation when the values of the cost function and the gamma were 0.1 and 0.1 respectively. The ANN (3-6-6-1) model performed better based on the 10-Fold Cross-Validation when the values of the batch size, epochs were 32 and 500 respectively, and the optimizer was the rmsprop (root mean square) as compared with the 5-Fold Cross-Validation when the values of the batch size, epochs were 25 and 500 respectively, and the optimizer was Adam, as seen from Tables 15 and 16 respectively. And also, the ANN (3-20-20-1) model performed better based on the 10-Fold Cross-Validation when the values of the batch size, epochs were 25 and 500 respectively, and the optimizer was Adam, as compared with the 5-Fold Cross-Validation when the values of the batch size, epochs were 25 and 100 respectively, and the optimizer was the Adam, as seen from Tables 15 and 16 respectively. Based on the 5- and 10- Fold Cross-Validation after grid searching the RF, SVM-RBF and ANN models achieved more than 80% as seen from Tables 15 and 16 respectively. The RF, SVM-RBF and ANN models outperformed the baseline model based on the 10-Fold Cross-Validation with grid search. Overall, in terms of accuracies, the ANN (3-6-6-1) model outperformed all the other models, achieving 85.19% and 86.42% based on the 5- and 10-Fold Cross-Validation respectively, after running grid search algorithms. Based on the findings of the ML algorithms, we have achieved the objective of this work to predict kyphosis disease. We compared the results of our ML models with the LR model, we found out that,LR is also capable in making clinical predictions, and for that matter, must not be overlooked, as can be verified from Table 17. Based on the small sample size used in this work, we took advantage of K-Fold Cross-Validation to evaluate the models, and also took advantage of grid search to obtained the best parameters for the models. For the ML Algorithms in making clinical predictions (kyphosis), the following are the implications based on our findings:  
1. In using RF model to making clinical predictions, it is better to set bootstrap to true, both gini or entropy can be tried as the criterion, and the value of 100 can be tried as the starting point for the number of trees. 2. In SVM-Linear model, given the value of gamma equals 1, the values of (0.1 or 1) can be tried as the cost function when making clinical predictions. 3. In SVM-RBF model, the highest value for the cost function can be 10, while the lowest value can be 1, and the highest value for the gamma can be 1 while the lowest value can be 0.1, when making clinical predictions to obtain high accuracies. 4. In using the SVM-Poly to making clinical predictions, the value of 0.1 can be tried for both the cost function and the gamma. 5. In making clinical predictions based on ANN model, the values of the batch size and the epochs can be tried with 32 and 500 respectively, and the optimizer can either be rmsprop or Adam.