## 3 Results
### Prediction
Images were preprocessed by resizing them to a consistent format, followed by feature extraction from the spatial and frequency domains using Fourier transform. Similarly, the DT model was constructed to classify patients based on medical data and image features. The results demonstrate that the ANN achieved superior performance compared to the DT. [PERSON] et al. [46] presented an ML methodology aimed at predicting mortality in patients afflicted with DFUs utilizing a dataset comprising 635 individuals who were admitted to the Diabetes, Nutrition, and Metabolic Diseases Clinic at Sf. Spiridon Emergency Clinical Hospital in Iasi, Romania. They developed two multilayer perceptron (MLP) models intended for predicting mortality at intervals of 5 years and 10 years after hospitalization. These models were architected with three hidden layers to enhance predictive performance. The design of the 5-year model included four neurons in the initial layer, a single neuron in the subsequent layer, and five neurons in the third layer. The 10-year model was slightly different, comprising five neurons in the first and second layers while incorporating four neurons in the third layer. Both computational models utilized ReLU activation functions and were subjected to training through a 10-fold cross-validation methodology to mitigate the risk of overfitting. The results indicate close levels of accuracy. [PERSON] et al. [47] examined the prediction of DF infections using deep learning techniques, employing a Kaggle dataset [36]. Critical features such as neuropathy, circulation issues, and skin thickness were extracted using MATLAB to enhance the prediction of diabetic foot ulcers. Participants were classified with and without foot ulcers, providing the basis for evaluating various classification algorithms. They compared neural networks, DT, regression model, and RF. Feature selection was a key component, with attributes extracted from foot images and medical data optimized to improve classifier performance. Models were assessed using metrics such as mean absolute error (MAE), root mean square error (RMSE), and relative absolute error (RAE). DT outperformed other methods, demonstrating the highest accuracy, making them the most effective model tested. [PERSON] et al. [16] introduced an ML model to predict amputation in patients with DFU. The study utilized a retrospective dataset of 150 patients from Beijing Shijitan Hospital. Researchers ensured that all included patients had DFU diagnoses above Wagner grade 1 and had undergone relevant diagnostic tests. The study employed the SVM algorithm to construct the model, chosen for its ability to handle complexity. The model development process followed multiple steps: (1) data preprocessing and variable selection usingLasso regression to identify critical predictors; (2) splitting the dataset into training and testing subsets using a five-fold cross-validation approach; (3) iterative model training and optimization to identify the best performing configuration; and (4) performance evaluation using metrics like the AUC. Researchers confirmed the model's calibration using Hosmer-Lemeshow testing, demonstrating its reliability. [PERSON] et al. [48] presented an AI-based DF prediction model integrating Traditional Chinese Medicine (TCM) tongue diagnosis and Western clinical data. They used ResNet-50 to extract deep features from tongue images and a fully connected layer (FCL) to collect numerical data such as BMI. The study utilized tongue images, plantar hardness, clinical features, and laboratory data from 391 patients collected at the Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine. The model achieved high accuracy, outperforming the model without tongue images. [PERSON] et al. [17] explored ML techniques to predict the recurrence risk of DFUs in elderly diabetic patients, aiming to enhance prevention strategies. Using a dataset of 138 patients, the authors analyzed several risk factors, such as age and wound size.