## 1 Introduction
Understanding these factors is essential for the prevention and management of diabetic foot complications. Diabetic neuropathy is associated with higher plantar foot temperatures, which can be measured non-invasively using infrared thermal imaging, indicating its potential as a tool for evaluating high-risk diabetic feet [22]. Furthermore, plantar pressure measurements are increasingly integrated into clinical practice, with evidence supporting their role in ulcer prevention and the importance of long-term monitoring to provide feedback on concern pressure levels [23]. [PERSON] et al. [24] emphasized that the simultaneous assessment of kinematics, kinetics, and plantar pressure can more accurately characterize the biomechanics of the diabetic foot, potentially helping to prevent foot ulcerations. The classification of plantar pressure distributions has proven useful in identifying diabetic patients at risk of foot ulceration and guiding the provision of preventive interventions, such as therapeutic footwear [25]. Changes in these parameters have been linked to diabetes-related foot complications, such as neuropathy and an increasedrisk of ulceration, ultimately contributing to the development of a diabetic foot [23]. However, there is a scarcity of studies that have investigated the potential of these measures to diagnose diabetes. This study introduces a novel approach to predicting diabetes by integrating temperature and plantar pressure data, a combination not extensively explored in the previous research. While the existing studies typically focus on either temperature or pressure independently, our work leverages both modalities to enhance the predictive accuracy. This multimodal approach provides deeper insights into foot health and potential complications. This work contributes to the field by not only demonstrating the limitations of single-modality analysis but also by showing how integrating multiple data sources can yield more robust machine learning models for clinical prediction tasks. Recently, the application of machine learning techniques in the prediction of diabetes has gained significant traction [26]. Machine learning uses computational algorithms to analyze large datasets, identify complex patterns, and make accurate predictions [27; 28]. The machine learning approaches to diabetes prediction are inherently data-driven, relying on diverse datasets that include a wide range of patient information such as clinical data, genetic markers, lifestyle factors, and physiological measurements. The integration of wearable sensor technologies has further enriched these datasets by providing real-time, continuous monitoring of the parameters relevant to diabetes [29; 30]. In the context of diabetes, the features of interest include blood glucose levels, insulin sensitivity, physical activity, dietary habits, and, as explored in this paper, thermal and pressure data from the feet. The machine learning models for diabetes prediction encompass a wide range of algorithms, such as Decision Trees, Support Vector Machines, Random Forests, and neural networks, among others. Each algorithm offers unique advantages and may be suited to different aspects of diabetes prediction. For example, deep learning models can effectively capture complex patterns in large datasets [31], while Decision Trees can provide more interpretable insights into risk factors [32; 33]. Few studies have examined the application of several available algorithms in tandem on the predictions [34; 35]. Evaluating the performance of machine learning models is a crucial step in the process. Metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC) are commonly used to assess the predictive capacity of these models. Despite these, there is a scarcity of previous studies that have investigated the potential of these measures in tandem to diagnose diabetes.