## 4 Conclusions  
This study presents a comprehensive investigation into the potential use of thermal and pressure data for the prediction of diabetes, spanning two sets of experiments that explored the relationships between these variables and their combined predictive power. The findings present promising avenues for the development of innovative strategies to enable early intervention and ultimately improve patient outcomes. The first set of experiments revealed the challenges in directly correlating thermal data with plantar pressure metrics. The weak correlations observed in the regression models suggest that thermal data, when used in isolation, may not be sufficient for accurately predicting the pressure values at specific anatomical points. However, presented new opportunities, allowing us to treat temperature and pressure as independent features in the subsequent diabetes prediction models. By doing so, we leveraged the unique strengths of each variable, leading to more robust and accurate predictive models. Figure 5: Performance comparison between the Extra Trees Classifier and Random Forest Classifier. **(a,c)** display the feature importance plots, with **(a)** highlighting the Extra Trees Classifier’s focus on thermal data and **(c)** illustrating the Random Forest Classifier’s balanced consideration of both temperature and pressure features. **(b,d)** depict the decision boundaries for the Extra Trees Classifier and Random Forest Classifier, respectively, showing how the models classify diabetic (1) and non-diabetic (0) cases based on these features. Random Forest’s mixed use of temperature and pressure data underscores its more comprehensive approach to predicting diabetes. In the second set of experiments, the integration of thermal and pressure data into machine learning models significantly enhanced the predictive capabilities for diabetes. The combined analysis demonstrated that using both temperature and pressure variables together provides a more comprehensive understanding of the physiological responses associated with diabetes. This approach paved the way for more accurate risk assessments and personalized diabetes management strategies. The integration of temperature and pressure data is expected to improve diabetes prediction because these two modalities provide complementary physiological insights. Temperature data can reveal early signs of inflammation, vascular issues, or tissue damage, which are common precursors to complications in diabetic patients, such as ulcers. On the other hand, plantar pressure measurements offer information about structural changes, foot deformities, and abnormal pressure distribution, which are also characteristic aspects of diabetic foot conditions. The previous approaches have primarily focused on one modality, limiting their ability to capture the complex interactions between these physiological factors. By combining both temperature and pressure data, the model can identify a broader range of diabetes-related abnormalities, improving the overall predictive accuracy and offering a more comprehensive assessment of foot health in diabetic patients. The study results indicate that, while the classifiers performed respectably with the standalone variables, significantly better results were achieved when the temperature and pressure data were combined. Specifically, the Logistic Regression model achieved the best performance among the classifiers, with an accuracy of 68.75% when using only plantar pressure data. In contrast, using only temperature data, the classifiers exhibited varying degrees of accuracy, the Naive Bayes model achieving the highest at 87.5%. Furthermore, when the models used both temperature and pressure data in tandem for the prediction of diabetes, the Extra Trees Classifier emerged as the most effective, achieving a precision of 93.75% and a perfect AUC score of 1. This model demonstrated strong performances on multiple metrics, including recall (0.875), precision (1), F1 score (0.9333), and kappa (0.875) values.