## 3 Results
### Diabetes Prediction from Temperature and Pressure Data
While each of these modalities provides valuable insights into the state of the foot, neither offers sufficient discriminatory power on its own to reliably identify diabetic conditions. The low predictive accuracy observed in both the temperature-only and pressure-only models underscores the limitations of single-modality approaches. These results strongly suggest that the integration of multiple data sources, such as combining temperature and pressure data, is necessary to improve the predictive performance. Additionally, incorporating other physiological or biomechanical features could further enhance the accuracy of machine learning models in clinical diagnostics. It is noteworthy that, according to Table 8, the Extra Trees Classifier, which performs exceptionally well with the combined data, only achieves a modest accuracy of 0.375 in the pressure-only experiment. The key insight arises when these results are compared to the earlier section where the models utilized both temperature and pressure data in tandem for diabetes prediction. Although some classifiers display competitive metrics in the individual-variable experiments, their collective predictive power falls short of the combined data prediction. The Extra Trees Classifier stands out as the most effective model in the combined experiment, achieving an accuracy of 0.9375 and a perfect AUC of 1.0. Figure 5 provides a comparative analysis of the Extra Trees Classifier and Random Forest Classifier, focusing on their performance in predicting diabetes using both thermal and pressure data. Figure 5a shows the feature importance for the Extra Trees Classifier, where certain features, primarily related to thermal data such as \"Hallux_Ref_Max\" and \"Hallux_Ref_Min\", are highlighted as the most significant contributors to the model's predictions. Figure 5c illustrates the feature importance for the Random Forest Classifier. Unlike the Extra Trees Classifier, the Random Forest Classifier shows a more balanced distribution of importance across both the temperature and pressure features. This indicates that the Random Forest model considers a mix of both types of data--temperature (e.g., \"Hallux_Min_30s\") and pressure (e.g., \"1 stM_APP\")--to be equally important in making accurate predictions. This balanced approach suggests that integrating both temperature and pressure data enhances the model's ability to predict diabetes effectively. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **Recall** & **Prec.** & **F1** & **Kappa** \\ \hline Extra Trees Classifier & 0.375 & 0.2969 & 0.5 & 0.4 & 0.4444 & \(-\)0.25 \\ Random Forest Classifier & 0.3125 & 0.3281 & 0.375 & 0.3333 & 0.3529 & \(-\)0.375 \\ Extreme Gradient Boosting & 0.4375 & 0.3594 & 0.375 & 0.4286 & 0.4 & \(-\)0.125 \\ Ada Boost Classifier & 0.4375 & 0.5 & 0.25 & 0.4 & 0.3077 & \(-\)0.125 \\ Gradient Boosting Classifier & 0.5 & 0.3594 & 0.5 & 0.5 & 0.5 & 0 \\ Naive Bayes & 0.5 & 0.5938 & 0.625 & 0.5 & 0.5556 & 0 \\ Logistic Regression & 0.6875 & 0.6719 & 0.75 & 0.6667 & 0.7059 & 0.375 \\ Decision Tree Classifier & 0.375 & 0.375 & 0.375 & 0.375 & 0.375 & \(-\)0.25 \\ Linear Discriminant Analysis & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0 \\ Ridge Classifier & 0.4375 & 0.4375 & 0.5 & 0.4444 & 0.4706 & \(-\)0.125 \\ Quadratic Discriminant Analysis & 0.625 & 0.625 & 0.25 & 1 & 0.4 & 0.25 \\ K Neighbors Classifier & 0.25 & 0.3359 & 0.375 & 0.3 & 0.3333 & \(-\)0.5 \\ SVM—Linear Kernel & 0.5 & 0.5 & 0 & 0 & 0 & 0 \\ Light Gradient Boosting Machine & 0.5 & 0.5 & 0 & 0 & 0 & 0 \\ Dummy Classifier & 0.5 & 0.5 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance of diabetes prediction models employing plantar pressure data. Figure 5b,d present the decision boundaries for the Extra Trees model and Random Forest Classifier, respectively. These boundaries visually demonstrate how each model differentiates between diabetic (1) and non-diabetic (0) cases based on the input features.