## 3 Results
### Segmentation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Study Aim** & **Data Type** & **Model Used** & **Code Availability** & **Hyperparameter** & **Training Protocols** & **Evaluation Metrics** \\ \hline
[18] & 2023 & \begin{tabular}{c} Predict \\ positive or \\ negative DFUs \\ \end{tabular} & \begin{tabular}{c} Image and \\ Numerical \\ \end{tabular} & ANN and DT & \begin{tabular}{c} Not \\ provided \\ \end{tabular} & \begin{tabular}{c} ANN: \\ Input features: 19, \\ Hidden layers: 2 \\ \end{tabular} & \
\begin{tabular}{c} Training: \\ 25\%, \\ 1-fold cross- \\ validation \\ (k = 10) \\ \end{tabular} &
\begin{tabular}{c} Accuracy \\ 1. ANN: 97\% \\ 2. DT: 93\% \\ \end{tabular} \\ \hline
[46] & 2023 & \begin{tabular}{c} Predict \\ mortality in \\ patients with \\ DFU \\ \end{tabular} & \begin{tabular}{c} Numerical \\ \end{tabular} & MLP & \begin{tabular}{c} Not \\ provided \\ \end{tabular} & \begin{tabular}{c} 5-year model: \\ 3 hidden layers (4-1-5 \\ neurons); \\ 10-year model: \\ 3 hidden layers (5-5-4 \\ neurons) \\ \end{tabular} & \
\begin{tabular}{c} Training: \\ 80\%, testing: \\ 20\%, 10-fold cross- \\ validation \\ (k = 10) \\ \end{tabular} &
\begin{tabular}{c} Training: \\ 77.2\% \\ 75.9\% \\ \end{tabular} \\ \hline
[47] & 2024 & \begin{tabular}{c} Predict \\ diabetes foot \\ infections and \\ ulcers \\ \end{tabular} & \begin{tabular}{c} Neural \\ networks, DT, \\ RE, regression \\ model \\ \end{tabular} & \begin{tabular}{c} Not \\ provided \\ \end{tabular} & Not mentioned & Not mentioned & \
\begin{tabular}{c} Model \\ comparison \\ using \\ classifier \\ \end{tabular} &
\begin{tabular}{c} Model \\ RMSE: 0.0005 \\ RMSE: 0.0005 \\ RMSE: 1.4285\% \\ \end{tabular} \\ \hline
[16] & 2024 & \begin{tabular}{c} Predict \\ diabetes foot \\ infections and \\ ulcers \\ \end{tabular} & \begin{tabular}{c} Numerical \\ \end{tabular} & \begin{tabular}{c} SVM \\ provided \\ \end{tabular} & \
\begin{tabular}{c} Not \\ provided \\ \end{tabular} &
\begin{tabular}{c} Data are \\ divided into \\ training sets \\ and test sets \\ by using \\ 5-fold cross- \\ validation \\ \end{tabular} & Accuracy: 82.4\% \\ \hline
[17] & 2024 & \begin{tabular}{c} Predict \\ amputation \\ risk in patients \\ with DFU \\ \end{tabular} & \begin{tabular}{c} Numerical \\ \end{tabular} & SVM & \begin{tabular}{c} Not \\ provided \\ \end{tabular} & \
\begin{tabular}{c} 5-fold \\ cross-validation \\ \end{tabular} &
\begin{tabular}{c} Data are \\ trained into \\ training sets \\ and test sets \\ AUC: 0.89 \\ \end{tabular} \\ \hline
[48] & 2024 & \begin{tabular}{c} Predict DF in \\ patients with \\ T2 DM using \\ Traditional \\ Chinese \\ Medicine and \\ Western medicine \\ \end{tabular} & \begin{tabular}{c} Numerical \\ and image \\ \end{tabular} & ResNet-50 & \begin{tabular}{c} Not \\ provided \\ \end{tabular} & \begin{tabular}{c} Pretrained on \\ ImageNet, \\ last three convolution \\ layers removed, \\ FCL for feature \\ extraction \\ \end{tabular} & \
\begin{tabular}{c} 311 images \\ for training, \\ 80 for testing, \\ 5 random \\ seed sets \\ \end{tabular} &
\begin{tabular}{c} Accuracy: \\ With tongue image: 0.95 \\ Without tongue image: \\ 0.92 \\ \end{tabular} \\ \hline
[18] & 2024 & \begin{tabular}{c} Predict \\ recurrence risk \\ of DFUs in \\ elderly diabetic \\ patients \\ \end{tabular} & \begin{tabular}{c} Numerical \\ \end{tabular} & SVM & \begin{tabular}{c} Not \\ provided \\ \end{tabular} & \begin{tabular}{c} C = 20, \\ degree = 2, \\ gamma: scale, \\ kernel type: linear \\ \end{tabular} & \
\begin{tabular}{c} Training: \\ 70\%, testing: \\ 30\%, 5-fold \\ cross- \\ validation \\ \end{tabular} &
\begin{tabular}{c} Accuracy: \\ SVM: 93\% \\ XGBoost 82\% \\ KNN: 82\% \\ RF: 82\% \\ DT: 79\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._encoder-decoder architecture. The results show that UPD had reliable performance across varied conditions, making it the preferred model for practical use. [PERSON] et al.