## 3 Results
### Prediction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Study Aim** & \begin{tabular}{c} **Data** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **Used** \\ \end{tabular} & \begin{tabular}{c} **Code** \\ **Availability** \\ \end{tabular} & **Hyperparameter** & \
\begin{tabular}{c} **Training** \\ **Protocols** \\ \end{tabular} &
\begin{tabular}{c} **Evaluation** \\ **Metrics** \\ \end{tabular} \\ \hline
[37] & 2023 & \begin{tabular}{c} Classify DFUs as \\ normal or \\ abnormal \\ \end{tabular} & Image & AWSg-CNN & \begin{tabular}{c} Not \\ provided \\ \end{tabular} & \
\begin{tabular}{c} Batch size, \\ learning rate: 0.01 \\ 25 epochs), \\ momentum: 0.9, \\ epochs: 2000 \\ \end{tabular} &
\begin{tabular}{c} Training: \\ 80\%, testing: \\ AUC: 99\% \\ \end{tabular} \\ \hline
[38] & 2023 & \begin{tabular}{c} Classify foot ulcer \\ images as normal \\ or abnormal \\ \end{tabular} & Image & \begin{tabular}{c} DRNN, \\ PFCNN \\ \end{tabular} & \
\begin{tabular}{c} Batch size: 32, \\ learning rate: 0.01 \\ 25 epochs), \\ momentum: 0.9, \\ epochs: 2000 \\ \end{tabular} &
\begin{tabular}{c} Training: \\ 50\%, testing: \\ 50\% \\ \end{tabular} & Accuracy: 99.32\% \\ \hline
[39] & 2024 & \begin{tabular}{c} Real-time \\ classification of \\ DFUs \\ \end{tabular} & Image & \begin{tabular}{c} DFU\_FNet, \\ DFU\_TFNet \\ \end{tabular} & \begin{tabular}{c} Not \\ provided \\ \end{tabular} & \begin{tabular}{c} Batch size: 32, \\ learning rate: 0.001, \\ epochs: 100 \\ \end{tabular} & \begin{tabular}{c} Transfer \\ learning rate: 0.001, \\ learning \\ \end{tabular} & \
\begin{tabular}{c} 3. GoogleNet: \\ accuracy: 91.93\% \\ \end{tabular} \\  & 2025 &
\begin{tabular}{c} IF score: 92.9\%, \\ 5. DFU\_FNet + SVM: \\ Accuracy: 94.71\% \\ \end{tabular} \\  & & & & & & \\  & & & & & 5. DFU\_TFNet: \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline
[40] & 2025 & DFU classification & Image & \begin{tabular}{c} RL, CPPN, \\ SVM, ELM, \\ ResNet50 \\ \end{tabular} & \begin{tabular}{c} Not \\ provided \\ \end{tabular} & \begin{tabular}{c} Learning rate: 0.1, \\ discount factor = 0.9 \\ \end{tabular} & \
\begin{tabular}{c} Reinforcement \\ learning \\ \end{tabular} &
\begin{tabular}{c} Clustering Efficiency: \\ Cluster 1: 71–88\% \\ Cluster 2: 85–97\% \\ Cluster 3: 90–98\% \\ \end{tabular} \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._hidden layer of 35 neurons and sigmoid activation, outperformed the other models across all evaluation metrics, outperforming KNN, SVM, and ANN. [PERSON] et al. [19] developed an ANN model to predict the prognosis of DFUs using clinical and lower extremity computed tomography angiography (CTA) data. They utilized data from 203 patients collected from the WoundCareLog database [45] from 195 hospitals in China. Patients were categorized into two groups based on their Wagner scores, a grading system used to classify the severity of DFUs based on the depth of the ulcer and the presence of infection. The ANN model, implemented using the multilayer perceptron algorithm, consisted of three layers: an input layer with ten neurons corresponding to the predictive factors, a hidden layer with two neurons for non-linear data interactions, and an output layer that classified patients. The ANN achieved superior performance compared to LR. [PERSON] et al. [18] evaluated two artificial intelligence models, an ANN and a DT, for predicting DFUs using medical data and foot images. A sample of 200 patients was included, 82 of whom had DFUs and 118 who did not, sourced from the National Institute of Diabetes and Endocrine Glands at Cairo University Hospital. The ANN model was designed with multiple layers: an input layer with nineteen nodes, two hidden layers with seven nodes each, and an output layer for binary classification: foot ulcer or non-foot ulcer.