## 1 Introduction
The primary objective of this study is to investigate the feasibility of using thermal data, pressure data, or a combination of both variables to predict the presence of diabetes. By analyzing the time series data of thermal and pressure measurements from a diverse group of individuals--including both diabetic and non-diabetic subjects--we aim to explore the potential predictive capabilities of these variables. The study employs machine learning algorithms to assess whether plantar pressure, plantar temperature, or a combination of both can effectively predict diabetes. For our analysis, we used a consolidated thermal time series that includes data from five anatomical points at the feet, combined with pressure data. In our study, the initial experiments aimed to investigate whether significant correlations could be established between thermal data and plantar pressure estimates using a variety of machine learning models for regression. Despite the use of sophisticated regression techniques, these models demonstrated sub-optimal performance in predicting pressure values from thermal data alone, as indicated by the low accuracy metrics and high error rates. The lack of a correlation suggests that the physiological processes reflected by the thermal measurements may not directly translate to the biomechanical indicators captured by the pressure data. Recognizing the limitations of this approach, we shifted our focus towards aggregating both data types--temperature and pressure--for the prediction of diabetes. This multimodal approach yielded significantly more encouraging results, with a notable improvement in predictive accuracy. The combination of these independent datasets allowedmachine learning models to leverage the complementary nature of thermal and pressure data, offering a more comprehensive understanding of the physiological changes associated with diabetes. By integrating these variables, we were able to achieve high levels of prediction accuracy, underscoring the importance of multimodal data fusion in medical diagnostics. We hypothesize that integrating these two physiological datasets will result in more accurate predictions. For example, when used independently, classifiers such as the Logistic Regression model have shown moderate accuracy with pressure data (around 68.75%), while temperature data can yield higher accuracy, with the Naive Bayes model reaching up to 87.5%. However, it is expected that the combination of pressure and temperature data will lead to significantly improved performance, with classifiers like the Extra Trees Classifier demonstrating stronger metrics, including precision, recall, and F1 scores. This study will also explore the importance of algorithm selection in optimizing prediction accuracy and the need to tailor models to different participant groups. By examining the performance of multiple machine learning models with both individual and combined data, this research seeks to highlight the potential of multimodal data integration in enhancing the accuracy of diabetes prediction. The rest of the paper is organized as follows: Section 2 outlines the methods used in our study, detailing the composition of the dataset, the experimental setup and protocol, and the data acquisition procedures. In Section 3, we present the results of the predictive models using machine learning techniques. Section 4 delves into the discussion and conclusions, summarizing the insights gained and their implications for diabetes prediction and management.