## 4 Discussion  
In previous works, other practitioners have compared and applied several machine learning algorithms to diagnose and predict diseases [7; 9; 10; 11; 12; 13; 32]. When it comes to a literature review on using machine learning algorithms in predicting other diseases, the work of [PERSON] et al. [12] has done \begin{table}
\begin{tabular}{c c} \hline \hline
**K-Fold** & **Accuracy** \\ \hline
1 & 0.7778 \\
2 & 0.6250 \\
3 & 0.7500 \\
4 & 1.0000 \\
5 & 0.8750 \\
6 & 0.7500 \\
7 & 0.7500 \\
8 & 0.8750 \\
9 & 0.7500 \\
10 & 0.7500 \\ Mean: 79.03\% & Std: 0.0969 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Accuracies achieved by performing 10-Fold Cross-Validation on the ANN (3-20-20-1) model. Figure 40: The best parameters with obtained after performing grid search with 10-Fold Cross-Validation on the ANN (3-20-20-1) model. Figure 39: The accuracies achieved by the Folds after performing 10-Fold Cross-Validation on the ANN (3-20-20-1) model. a good job. In the work of [PERSON] et al. [13], they modeled an ANN to detect heart disease using 13 inputs, 2 hidden nodes and 1 output. Also, the work of [PERSON] [14] used ANN to predict heart disease, which achieved 88% accuracy. It was further stated that ANN is broadly used in medical diagnosis and healthcare systems as result to the predictive power it possesses as a model as confirmed by [PERSON] et al. [12]. The work of [PERSON] et al. [15] compared SVM and ANN to predict heart disease, they stated that, ANN outperformed the SVM model to predict the disease. Furthermore, the work of [PERSON] et al. [2] compared several machine learning algorithms such as SVM, ANN, KNN, DT for breast cancer detection. They have performed an in-depth literature review on various machine learning algorithms for further reading. They concluded that SVM outperformed all the other models, which achieved an accuracy of 99.8% to detect the breast cancer disease. Also, the work of [PERSON] et al. [16] compared several machine learning algorithms on a case-based retrieval approach of clinical breast cancer patients. They concluded that the RF model showed the maximum accuracy of 99%. The work of [PERSON] et al. [18] compared the performance of naive-Bayesian, SVM, logistic regression, C4.5 decision tree, and random forest methods in predicting the medical costs of spinal fusion. They concluded that the random forest algorithm had the best predictive performance in comparison to the other techniques, achieving an accuracy of 84.30%, a sensitivity of 71.4%, a specificity of 92.2%, and an AUC of 0.904. ly. This predictive power of RF model can be noticed in this current research work in terms of accuracy as shown in Tables 15 and 16. And lastly, in the work of [PERSON] et al. [8], they used machine learning techniques to predict spinal abnormalities based on KNN and RF models. They concluded that the KNN model which achieved an accuracy of 85.32% outperformed the RF model which had an accuracy of 79.57%. Therefore, the purpose of this current research was to compare the performances of three most used and famous machine learning algorithms such as the RF, SVM, and ANN in the classification and prediction of kyphosis disease based on a biomedical dataset. At first, we performed 5-Fold and 10-Fold Cross-Validations using Logistic Regression as a baseline model to compare with our ML models in terms of K-Fold CV without performing grid searching. The idea was based on 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. We observed a mean accuracy of 81.75% as seen from Figure 15, based on the 5-Fold Cross-Validation after evaluating the LR model. After performing 10-Fold Cross-Validation, the LR model achieved a mean accuracy of 79.42% as seen from Figure 15.