## 2 Materials and Methods
### Proposed Approach  
This study illustrates the interaction between the functional design of the proposed predictive model and other significant DL and ML applications, the tuning and control of Hyperparameters, and model testing using K-fold cross-validation. The proposed system has classified people with Kyphosis sickness and healthy individuals. Different ML and DL models for Kyphosis sickness were examined for their results. The design used DL models, DNN, and popular ML models, LR, NB, RF, SVM, and KNN. Figure 1 shows the system's framework in action. A dataset for Figure 1 associated with the Kyphosis sickness of the patients was chosen. During the preparation stage, all inconsistencies that might have existed in the dataset when the data was acquired must be eliminated. The next step was to select the testing mode and the classification methods used during implementation. The previously described classification algorithms can now be put to use with the aid of Hyperparameter tuning methods. A model can be trained using existing data, making it capable of fitting the model parameters. Other sensible parameters, referred to as Hyperparameters, cannot be immediately learned using the standard training procedure. Sometimes they are set before the training process itself starts. These factors represent categorical model requirements, \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{3}{c}{**Prediction**} \\ \cline{2-4}  & & 0 & 1 \\ \cline{2-4}  & 0 & TN & FP \\ \cline{2-4}  & 1 & FN & TP \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion Matrix. Figure 1: The framework of the proposed system for detecting Kyphosis disease. such as how complex the model is or how quickly it should learn [32]. The model's performance can be maximized using that set of Hyperparameters, which minimizes a predetermined loss function and results in better results with fewer errors. When there is a lack of information or information that is not evenly distributed, the K-fold is often used. Sampling also affects K-fold cross-validation. When tackling classification challenges with imbalanced class distributions, stratified K-fold became more popular than K-fold. As a result, stratified K-fold cross-validation was used in the current study to evaluate the models. To determine which algorithm is the best and delivers the highest level of accuracy when forecasting the outcome, the study will finally assess and contrast each of the produced algorithms. The main steps in the proposed model are summarized below:  
1. The dataset for the Kyphosis disease was obtained from Kaggle. This dataset on Kyphosis contains information on patients who underwent corrective surgery for the disease. The Kyphosis data set has 81 rows and 4 columns regarding adolescents who underwent spinal fusion surgery. The dataset consists of one outcome, Kyphosis, and three inputs, age, variety, and start. These 81 samples are then divided into a training set, a validation set, and a test set using a ratio of 6:1:2. 2. The data were preprocessed using the Scikit-Learn program. The label encoder was used to divide the Kyphosis region into 0s and 1s. To help the ML and DL models work better, the data were scaled using the Standard Scaler function of the Sklearn library. 3. A model is trained to produce new observations or classifications based on the data it is provided through the classification process in machine learning and deep learning. The proposed models used the LR, NB RF, SVM, KNN, and DNN algorithms for training and result prediction. 4. Certain important parameters, known as Hyperparameters, are difficult to learn using conventional training. These parameters categorize the model's fundamental features, such as complexity or learning rate. Hyperparameter tuning was used in this study to select the most efficient parameters. With that particular collection of Hyperparameters, the model's performance is maximized, a preset loss function is minimized, and better outcomes with fewer errors are obtained. 5.