## 3 Results
### Diabetes Prediction from Temperature and Pressure Data  
The lack of a significant correlation between the consolidated thermal data and pressure metrics, as observed in the previous analysis, suggests that these two variables may not be directly related or reflect a causal relationship. However, this apparent disconnect between the thermal data and pressure values does not diminish their individual predictive potential. On the contrary, it opens the door to treating temperature and pressure as independent features in the context of diabetes prediction. By considering thermal and pressure data as separate, uncorrelated inputs, it becomes possible to harness the unique predictive capabilities of each variable. Temperature data may capture specific physiological responses, such as inflammation or altered blood flow, that are indicative of diabetic conditions, while pressure data could reflect biomechanical abnormalities associated with diabetes, such as altered gait or foot structure. Together, these independent features have the potential to provide a more comprehensive and accurate prediction model for diabetes. In this following section, we explore the application of these variables in diabetes prediction. By leveraging their individual strengths as separate features within machine learning models, we aim to enhance the accuracy and reliability of diabetes diagnosis, as demonstrated in the first study. The primary objective of this study is to assess the combined predictive potential of temperature and pressure data to diagnose diabetes. To achieve this, a set of machine learning models, facilitated by the PyCaret library, were employed to predict the diabetes status for each instance in the dataset. Table 8 provides an overview of the results obtained from the diabetes prediction task across the tested machine learning models. The table is organized to reflect a performance hierarchy, listing the models in descending order according to their predictive effectiveness. This arrangement begins with the highest-performing model and progresses to those with comparatively lower predictive capabilities. Upon reviewing the performance metrics across the various machine learning models, several key insights emerge. The Extra Trees Classifier stands out as the top performer, demonstrating the highest accuracy, AUC, recall, precision, F1 score, and kappa values, which collectively indicate its strong and consistent performance across multiple metrics. In contrast, models such as the Random Forest Classifier, Extreme Gradient Boosting, and Naive Bayes show variability in their performance across the different metrics, suggesting differences in how these models address various aspects of the prediction task. Certain models, such as Quadratic Discriminant Analysis and K Neighbors Classifier, exhibit lower accuracy, recall, precision, and F1 scores, which may reflect limitations in their predictive capabilities for this particular dataset. Additionally, models like the SVM with a Linear Kernel, Light Gradient Boosting Machine, and Dummy Classifier display zero performance across all the metrics, indicating that they may not be suitable for this specific prediction task. In general, the data highlight a diverse range of model performances, shedding light on potential candidates that excel in predicting diabetes based on the combined temperature and pressure data. Tables 9 and 10 summarize the results of two distinct experiments, each focusing on predicting diabetes using temperature or pressure as independent variables. Although several classifiers achieve respectable metrics when applied to individual variables, their performance does not match the superior precision observed with the combined data prediction presented in Table 8. In the experiment utilizing temperature data, the classifiers exhibit varying degrees of accuracy, with the Naive Bayes model achieving the highest accuracy at 0.875.