## 3 Results
### Diabetes Prediction from Temperature and Pressure Data
Similarly, in the pressure-only experiment, the Logistic Regression model attains an accuracy of 0.6875, marking the best performance among the classifiers for this experiment. Furthermore, it is important to observe that the F1 score, recall, and precision metrics are closely aligned in the table for diabetes prediction using both the combined pressure and temperature features, as well as in the individual predictions. This consistency across the metrics indicates that the model provides a balanced response between both classes. In particular, the similarity of these metrics suggests that the model is not biased toward one class over the other, effectively managing the trade-off between false positives and false negatives. This balance is particularly important in clinical prediction tasks, where misclassifications can have significant consequences. The fact that these performance met \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **Recall** & **Prec.** & **F1** & **Kappa** \\ \hline Extra Trees Classifier & 0.9375 & 1 & 0.875 & 1 & 0.9333 & 0.875 \\ Random Forest Classifier & 0.75 & 0.875 & 0.625 & 0.8333 & 0.7143 & 0.5 \\ Extreme Gradient Boosting & 0.75 & 0.8438 & 0.5 & 1 & 0.6667 & 0.5 \\ Ada Boost Classifier & 0.625 & 0.7812 & 0.375 & 0.75 & 0.5 & 0.25 \\ Gradient Boosting Classifier & 0.625 & 0.625 & 0.375 & 0.75 & 0.5 & 0.25 \\ Naive Bayes & 0.6875 & 0.7734 & 0.625 & 0.7143 & 0.6667 & 0.375 \\ Logistic Regression & 0.5625 & 0.6406 & 0.375 & 0.6 & 0.4615 & 0.125 \\ Decision Tree Classifier & 0.625 & 0.625 & 0.375 & 0.75 & 0.5 & 0.25 \\ Linear Discriminant Analysis & 0.6875 & 0.875 & 0.5 & 0.8 & 0.6154 & 0.375 \\ Ridge Classifier & 0.6875 & 0.6875 & 0.5 & 0.8 & 0.6154 & 0.375 \\ Quadratic Discriminant Analysis & 0.125 & 0.125 & 0.25 & 0.2 & 0.2222 & \(-\)0.75 \\ K Neighbors Classifier & 0.4375 & 0.5391 & 0.625 & 0.4545 & 0.5263 & \(-\)0.125 \\ 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 8: Comparative performance of diabetes prediction models employing combined thermal time series and plantar pressure data. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **Recall** & **Prec.** & **F1** & **Kappa** \\ \hline Extra Trees Classifier & 0.8125 & 0.9375 & 0.875 & 0.7778 & 0.8235 & 0.625 \\ Random Forest Classifier & 0.75 & 0.9375 & 0.75 & 0.75 & 0.75 & 0.5 \\ Extreme Gradient Boosting & 0.75 & 0.9062 & 0.75 & 0.75 & 0.75 & 0.5 \\ Ada Boost Classifier & 0.8125 & 0.9297 & 0.875 & 0.7778 & 0.8235 & 0.625 \\ Gradient Boosting Classifier & 0.8125 & 0.7812 & 0.875 & 0.7778 & 0.8235 & 0.625 \\ Naive Bayes & 0.875 & 0.875 & 0.75 & 1 & 0.8571 & 0.75 \\ Logistic Regression & 0.8125 & 0.9375 & 0.875 & 0.7778 & 0.8235 & 0.625 \\ Decision Tree Classifier & 0.8125 & 0.8125 & 0.875 & 0.7778 & 0.8235 & 0.625 \\ Linear Discriminant Analysis & 0.8125 & 0.8984 & 1 & 0.7273 & 0.8421 & 0.625 \\ Ridge Classifier & 0.9375 & 0.9375 & 1 & 0.8889 & 0.9412 & 0.875 \\ Quadratic Discriminant Analysis & 0.4375 & 0.4375 & 0.625 & 0.4545 & 0.5263 & \(-\)0.125 \\ K Neighbors Classifier & 0.8125 & 0.875 & 0.75 & 0.8571 & 0.8 & 0.625 \\ SVM—Linear Kernel & 0.5 & 0.5 & 1 & 0.5 & 0.6667 & 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 9: Performance of diabetes prediction models employing thermal time series data. rics remain comparable across different feature sets (pressure, temperature, and combined), further supports the robustness of the model and highlights that both modalities contribute meaningful information for predicting diabetic conditions. The findings from this experiment highlight the challenges associated with predicting diabetes based solely on temperature or pressure data.