###### Abstract  
Diabetes is a chronic disease that is escalating day by day and requires 24/7 continuous management. It may cause many complications, precisely when a patient moves, which may risk their and other drivers' and pedestrians' lives. Recent research shows diabetic drivers are the main cause of major road accidents. Several wireless non-invasive health monitoring sensors, such as wearable continuous glucose monitoring (CGM) sensors, in combination with machine learning approaches at cloud servers, can be beneficial for monitoring drivers' diabetic conditions on travel to reduce the accident rate. Furthermore, the emergency condition of the driver needs to be shared for the safety of life. With the emergence of the vehicular ad-hoc network (VANET), vehicles can exchange useful information with nearby vehicles and roadside units that can be further communicated with health monitoring sources via GPS and Internet connectivity. This work proposes a novel approach to the health care of drivers' diabetes monitoring using wearable sensors, machine learning, and VANET technology. Several machine learning (ML) algorithms assessed the proposed prediction model using the cross-validation method. Performance metrics precision, recall, accuracy, F1-score, sensitivity, specificity, MCC, and AROC are used to validate our method. The result shows random forest (RF) outperforms and achieves the highest accuracy compared to other algorithms and previous approaches ranging from 90.3% to 99.5%. diabetes; wearable body sensors; machine learning; intelligent vehicular system; health; technology +
Footnote †: journal:diabetes [3]. There is no uncertainty that this disturbing figure needs incredible attention. Figure 1 demonstrates the expected diabetes cases. There is a need to monitor the blood glucose level to avoid future complications continuously. Monitoring blood glucose at home became a reality in the late 1970s when [PERSON] developed the first self-monitoring BG (SMBG) meter, known as the Ames reflectance meter (ARM) [4]. SMBG frameworks are versatile gadgets that measure BG, typically 3 to 4 times daily, using a drop of blood on slim glucose oxidase-based strips. Due to inadequate sampling frequency, it was apparent that SMBG cannot disclose all severe incidents arising in daily life, e.g., severe hyperglycemia and hypoglycemia. In current years, blood glucose (BG) monitoring has been transformed by the advancement of continuous glucose monitoring (CGM) sensors. These non-invasive wearable sensors analyze glucose attentiveness almost continuously, e.g., every 1 to 5 min. The first wearable CGM sensor model was presented in 1999 [5], and since then, gadgets have developed quickly. Today, CGM sensors implant numerous features that can authorize a user's ability to make intelligent decisions, e.g., taking drugs, exercising to manage hyperglycemia, and eating foods that offset hypoglycemia. Modern CGM devices can show the existing blood glucose level in real-time and provide visual alerts for hyper/hypoglycemia. Advanced sensors utilize non-invasive or minimally invasive needle sensors, typically injected in the subcutaneous tissue, on the arm, or in the abdomen, as presented in Figure 2, which quantifies an electrical flow signal generated by the glucose oxidase response. This sign corresponds to the glucose absorption accessible in the interstitial fluid, which is then changed into a glucose concentration by an adjustment strategy generally performed twice daily. Figure 1: Estimate of expected diabetes cases year-wise. Figure 2: (**a**) Non-invasive abdomen sensor; (**b**) non-invasive arm sensor; (**c**) minimally invasive interstitial fluid transmitter glucose sensor. Several medical conditions and diseases require continuous monitoring of physiological data regularly, even when the diabetic patient is on the move. Studies show that complications caused by diabetes are the main cause of major road accidents [6].