## 4 Discussion
Lastly, based on the K-Fold Cross-Validation, without Grid Searching, the ANN (3-6-6-1) model achieved a mean accuracy of 79.12% based on the 5-Fold Cross-Validation as seen from Table 17.1 With the 10-Fold Cross-Validation, the ANN (3-6-6-1) model achieved a mean accuracy of 79.03% as seen from Table 17.2 We can observe a decrease in accuracy from 79.12% to 79.03% after the 10-Fold CV by the ANN (3-6-6-1) model as shown in Table 17.3 Also, the ANN (3-20-20-1) model achieved a mean accuracy of 79.12% based on the 5-Fold Cross-Validation as seen from Table 17.4 With the 10-Fold Cross-Validation, the ANN (3-20-20-1) model achieved a mean accuracy of 79.03%. We can observe a decrease in accuracy from 79.12% to 79.03% after the 10-Fold CV by the ANN (3-6-6-1) model as shown in Table 17.5 By comparing the ANN models with the LR model, based on the K-Fold Cross-Validation, without grid search, the LR model outperformed both the ANN models as seen from Table 17.6  
With the analysis in this current work, based on performing 5- and 10-Fold Cross-Validation without grid searching (as summarized in Table 17), we then state that the assumption concluded in the work of [PERSON] et al. [33] that they found no evidence of superior performance of machine learning over Logistic Regression when performing clinical prediction modeling is somehow quite true. We observed that, among the ML algorithms, the SVM-RBF outperformed all the other ML models, which achieved an accuracy of 82.62% based on the 5-Fold Cross-Validation with grid searching as \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **5-Fold Cross-Validation (ACC\%)** & **10-Fold Cross-Validation (ACC\%)** \\ \hline LR & 81.75 & 79.42 \\ RF & 79.01 & 77.76 \\ SVM-Linear & 79.25 & 77.24 \\ SVM-RBF & 82.62 & 78.87 \\ SVM-Poly & 76.42 & 76.96 \\ ANN (3-6-6-1) & 79.12 & 79.03 \\ ANN (3-20-20-1) & 79.12 & 79.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: A Comparison of the accuracies achieved by the ML models and the baseline model (LR) after performing 5- and 10-Fold Cross-Validation without running grid search algorithm. shown in Table 17. Whereas, based on the 10-Fold Cross-Validation without grid searching, the ANN models outperformed the other ML models, by which both the ANN models achieved an accuracy of 79.03% as shown in Table 17.  
The second category of our experiment was to improve on the performances of the ML models, and then find the best parameters with the highest accuracies. We achieved these by performing grid search with 5- and 10-Fold Cross-Validation. After running the grid search algorithms, the performance of the RF model improved from 79.01%, and 77.76% to 80.25% based on both the 5- and 10-Fold Cross-Validation as seen from Tables 15 and 16 respectively. We observed that, based on 10-Fold CV with grid search, the RF model outperformed the LR model as seen from Tables 15 and 16 respectively. Among the SVM classifiers, we also observed that, the accuracies of the SVM-Linear did not improve significantly after performing the grid search with the K-Fold CV (as shown in Tables 15 and 16) to that of the K-Fold CV without grid search as shown in Table 17. Again, the LR model outperformed the SVM-Linear model. We observed an improvement of the SVM-RBF model from 82.62%, and 78.87% to 82.72, and 85.19% based on the 5- and 10-Fold Cross-Validation as seen from Tables 15 and 16 respectively. By comparing the SVM-RBF model with the LR model, after performing grid search with K-Fold CV, the SVM-RBF model then outperformed the LR model as seen from (Tables 15 and 16) and Table 17 respectively. Finally, among the ANN models, the accuracies of the ANN (3-6-6-1) improved from 79.12%, and 79.03% to 85.19%, and 86.42% based on the 5- and 10-Fold Cross-Validation as seen from Tables 15 and 16 respectively. Also, the accuracies of the ANN (3-20-20-1) improved from 79.12%, and 79.03% to 83.95%, and 85.19% based on the 5- and 10- Fold Cross-Validation as seen from Tables 15 and 16 respectively.