## 1 Introduction  
Diabetes mellitus, a common chronic metabolic disorder characterized by elevated blood glucose levels, presents significant health challenges globally [1]. Its considerable impact on healthcare systems highlights the urgent need for early diagnosis and effective management to prevent complications and improve patient outcomes [2]. Among the complications associated with diabetes, diabetic foot problems are particularly severe, with the potential to lead to amputation in the most extreme cases [3]. Traditionally, diabetes diagnosis has relied on clinical assessments, blood tests, and self-reported symptoms. However, recent advances in technology have paved the way for innovative approaches to predicting and managing diabetes more efficiently [4]. Further complications, such as neuropathy, which causes nerve damage and loss of sensation, can lead to a diabetic foot, where minor injuries can go unnoticed and progress to severe infections or ulcers due to poor blood circulation and reduced healing capacity. Accurate and timely prediction is essential for early intervention and effective disease management [5]. In recent years, wearable sensor technologies have gained popularity as tools for monitoring physiological data, showing promise in identifying new indicators to predict diabetes [6; 7]. Notably, the use of thermal and pressure data has shown potential in improving the accuracy of diabetes diagnosis and treatment. For instance, measuring the plantar temperature can reveal differences in the soles of diabetic feet, with the ability to detect ulcers and necrosis with accuracies of 90% and 88%, respectively [8]. Infrared thermography has also proven to be effective in detecting temperature variability in the feet of diabetic patients, aiding in the early diagnosis and prevention of lesions in affected areas [9]. Furthermore, regression analysis has shown the potential to accurately predict the maximum plantar pressure in patients with type 2 diabetes mellitus, which is crucial for the prevention and early detection of diabetic foot complications [10]. However, studies that combine temperature and pressure data are limited. For example, [PERSON] et al. [11] found no significant correlation between plantar temperatures and triaxial plantar stresses in individuals with diabetes. This lack of correlation suggests that these variables can be effectively combined as independent attributes in machine learning models for the prediction of diabetes, potentially leading to more robust predictive models, as is also discussed in this paper. Wearable sensor technologies, such as those integrated into shoes or insoles, have emerged as promising tools for the continuous monitoring of physiological parameters relevant to the management of diabetes [12; 13; 14; 15]. These non-invasive technologies enable continuous data collection, facilitating the early detection of potential problems, including those related to diabetic foot complications [16]. Of particular interest for the prediction of diabetes are the thermal patterns and pressure distribution measured by these devices [17]. Thermal imaging techniques are used to visualize the distribution of skin temperature, which can indicate underlying metabolic processes and pathological conditions. This non-invasive diagnostic method uses the principles of heat transfer and physiological responses of the body to detect temperature variations that can signal health problems [18]. Meanwhile, several studies have indicated that insole systems that measure plantar pressure can be beneficial in managing diabetic foot health by reducing ulcer recurrence, lowering plantar stress, helping to detect early complication, and improving gait and weight distribution [19; 20; 21]. The relationship between plantar pressure, temperature, and diabetic foot complications is an emerging area of research. Diabetic neuropathy often leads to foot ulceration due to a combination of elevated temperatures, loss of sensation, and abnormal pressure distribution.