## 2 Methods
### Feature Description and Data Structure
The cross-validation framework ensured that each participant's data were used both in training and validation across different folds, mitigating overfitting and providing a robust estimate of model performance. Analysis of Correlation Between Individuals' Feet  
To explore the validity of treating each foot as an independent instance in the dataset, a correlation analysis was performed across both feet of each individual and between feet of different individuals. The correlation index was calculated for each pair of feet, and the results are displayed in Figure 4, which shows the correlation matrix of pressure and temperature data. The correlation matrix reveals that, on average, the correlation between feet from different individuals is relatively high, with a mean value of approximately 0.86. This suggests that, while there are similarities between individuals, there is still sufficient variability across the dataset to justify treating each foot as an independent instance. Such variability is important for machine learning models to generalize effectively, even when  
Figure 4: Correlation index matrix representing the relationship between temperature and pressure features across different individuals. The matrix shows correlation coefficients for pressure and temperature data between the left and right feet of each individual, as well as across different individuals. the left and right feet of the same individual are included in both the training and test sets across different folds. Although the data demonstrate subtle similarities between feet, particularly in healthy individuals, these subtle differences are likely to capture clinically meaningful variations, especially in diabetic patients. In this population, asymmetry between feet may reflect complications such as neuropathy, making it important for the model to learn from these discrepancies. This approach enables the model to better generalize across a wider range of physiological conditions, improving its ability to detect early signs of complications. By treating each foot as independent, we effectively increase the dataset size, which is particularly important in studies with small datasets. Given the high correlation values, the model benefits from additional data points while still capturing enough variation to avoid overfitting. Moreover, the use of 5-fold cross-validation ensures that the model is evaluated on a wide range of data splits, further reducing the risk of overfitting and ensuring robustness. While potential bias due to symmetry in healthy individuals is acknowledged, the correlation analysis suggests that this bias is minimal. From a machine learning perspective, the variability present in the dataset justifies the approach, particularly given the asymmetry often observed in diabetic patients. As a result, treating each foot as an independent instance not only increases the dataset's robustness but also enhances the generalization of the machine learning models by exposing them to a broader range of conditions. **Rationale for Independent Foot Analysis:** While treating each foot as an independent data point increases the dataset size and helps to capture key asymmetries in diabetic individuals, we acknowledge that this approach may introduce bias, particularly in healthy individuals where greater symmetry between left and right feet is typically observed. Inclusion of both feet in both the training and testing sets could lead to inflated model performance as the symmetry between feet in healthy individuals might not reflect true independent variability. Results should be interpreted with caution, especially in cases where foot symmetry is expected. The essential variations in patients likely include differences in temperature distribution, pressure patterns, and structural abnormalities between the left and right feet. These variations are particularly important in diabetic patients, where asymmetries can indicate complications like ulcers, neuropathy, or other foot pathologies.