## 4 Conclusions
Other models, such as the Random Forest Classifier and Extreme Gradient Boosting, showed varied performances across these metrics, highlighting differences regarding how they handled the prediction task. These results highlight the importance of integrating temperature with plantar pressure measurements in monitoring the activities of daily living regarding diabetes. The findings also emphasize the need to evaluate multiple classification algorithms to determine which is the most accurate for predicting diabetes in different participant clusters. While this study treats each foot's data as an input to the model, we acknowledge that this approach could introduce potential bias, especially in healthy individuals, where symmetry between the left and right feet is generally observed. However, asymmetries in the foot data between individuals with diabetes exist, which can provide valuable diagnostic information. To further investigate this, we calculated the correlation between feet across different individuals, revealing that, while there is a high correlation between the feet of the same person, subtle variations exist across individuals. These findings support the use of cross-validation with independent foot data as the dataset still exhibits enough variability to provide meaningful insights. However, this may reduce the variability in healthy individuals and could potentially overestimate the model's performance in specific cases. Furthermore, the study underscored the complexity of pressure distribution in feet and highlighted the importance of comprehensive data integration and advanced feature engineering. The inability of regression models to accurately predict pressure from consolidated temperature data emphasized the need for a more nuanced approach that considers the independent contributions of each variable to diabetes prediction. The introduction of additional modalities has the potential to overcome the limitations observed in this correlation analysis, paving the way for more robust and accurate pressure prediction models. **Limitations and Future Work:** One limitation of this study is the potential bias introduced by treating each foot from the same individual as an input to the model. In healthy individuals, where foot symmetry is generally present, this may lead to an overestimation of the model performance. Future studies should examine whether treating both feet as correlated data is a more appropriate method when symmetry is expected. Furthermore, exploring whether foot symmetry or asymmetry persists across different demographic factors such as gender, ethnicity, and foot dominance would add depth to the analysis. Additionally, future research should explore and contrast the condition of treating both feet from the same individual as independent instances. This approach should ensure that the data from the same individual are not included in both the training and testing datasets, which would enhance the robustness and generalizability of the model by avoiding potential bias introduced by symmetry or shared characteristics between the feet. Looking forward, future research should also focus on refining the feature engineering techniques and optimizing the model selection to fully harness the predictive potential of these combined variables. Additionally, further studies are essential to explore how insoles and other biomechanical factors impact prediction models, which could lead to new therapeutic interventions and enhance the accuracy of diabetes management tools. By continuing to build on the insights gained from this study, there is considerable potential to advance the field of diabetes prediction and improve the quality of life for individuals affected by this condition. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a17110519/s1](https://www.mdpi.com/article/10.3390/a17110519/s1)]([https://www.mdpi.com/article/10.3390/a17110519/s1](https://www.mdpi.com/article/10.3390/a17110519/s1)).