# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 3 Materials and Methods
### Graphical Web Interface  
This section describes the development process of the graphical interface. MySQL was used to develop the platform's database because it easily handles large volumes of data; this model consists of several tables, each with different functionality within the web application (Figures 6 and 7). For the development of the \"VITA-D\" graphical interface tool, we used _BootstrapStudio_, which allows integration with the \"Nice Admin\" template obtained from _BootstrapMade_. These template elements facilitated customization and responsive design. The designs made in _BootstrapStudio_ were exported as HTML files. These files were incorporated into a web development environment based on _Django_, a _Python_ framework for web application development. In the Django structure presented in Figure 8, specific folders were created: templates to store the HTML files and static ones to manage resources such as CSS, JavaScript, and image files necessary for the styling and interactive functionality of the interface. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Description** & **Equation** \\ \hline \multirow{8}{*}{Kappa value} & [PERSON]’s kappa measures the agreement between two raters that each classify \(N\) items into \multicolumn{1}{c}{C mutually exclusive categories. According to \multirow{4}{*}{Kappa value}} & \multirow{4}{*}{\(k=\frac{P_{0}-P_{c}}{1-P_{c}}\)} & \\  & convention, a \(\kappa\)-value of 0 to 0.2 indicates slight agreement; & \\  & 0.2 to 0.4 fair agreement; 0.4 to 0.6 moderate agreement; 0.6 to 0.8 substantial agreement; and & \\  & 0.8 to 1.0 almost & \\  & perfect agreement [59]. & \\ \hline \multirow{8}{*}{_AUIC-ROC_} & Measures the ability of the \multicolumn{1}{c}{classifier to distinguish} & \\  & between classes. It represents & \\ \cline{1-1}  & the area under the ROC curve, & \multirow{2}{*}{\(AUC\int_{0}^{1}tp\)} & Equation (9) \\ \cline{1-1}  & where higher values indicate & \\ \cline{1-1}  & better performance. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**True Class** & \multicolumn{2}{c}{**Predicted Class**} \\  & \multicolumn{2}{c}{**C\({}_{1}\) C\({}_{2}\)**} \\ \hline C\({}_{1}\) (Deficiency) & TP & FN \\ C\({}_{2}\) (Sufficiency) & FP & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix allows for visualization of the performance of a classification algorithm. It shows the TP: true positive; FN: false negative; FP: false positive; TN: true negative. Figure 8: Project structure VITA_D_web. (**a**) Front-end Vita_D_Web. (**b**) Back-end Vita_D_Web (main folder): contains all the back-end logic and configuration of the Django project. init.py: an empty Python file that contains a Python directory. package.asgi.py and wsgi.py: configuration files for the ASGI and WSGI gateways, which help Django communicate with the web server. settings.py: Django project settings, including database connection, installed applications, middleware, and templates, among others. urls.py: defines the URL patterns for the web application that directs incoming requests to the application’s functions. views.py: contains the view functions that handle what end users request from the server. models.py: describes the database in Django framework format. queries.py: stores queries to the database. Figure 7: MySQL database design. _Users table_ manages user authentication. The _patient’s table_ records all relevant patient information. _Medical History Table_ assigns each patient a record when entering their data, linking the medical history with the user. The _sessions table_ is used to maintain access control and manage user sessions. The _prediction table_ stores the prediction percentages obtained from the machine learning models. The _metrics table_ stores the results of the accuracy of the predictions. The _risk factors table_ represents variables associated with each patient that may influence the risk of vitamin D deficiency. Each risk factor is recorded individually.