# Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence
## 3 Methodology  
### The Proposed System  
Diabetes is caused by the pancreas not producing enough insulin or by the body's inability to consume the insulin produced. According to reports generated by WHO, the percentage of people affected by diabetes in the US is very high. Continuous glucose monitors (CGMs) are becoming an increasingly popular way to monitor blood glucose. The method works by inserting a right needle wearable device into the skin that monitors blood sugar levels (tissue levels). The CGM reports blood glucose trends and provides alerts to stop hypoglycemia and supply the best glycemic monitoring [33]. The data are connected to the smartphone via Bluetooth, and then the information is connected to the cloud computing (internet or network) server. For people with diabetes, if the blood sugar levels rise, the system immediately generates various alerts. If the percentage of diabetes exceeds the permissible limit, especially for diabetic patients, the emergency will be called directly. This work illustrates how machine learning is implemented to predict diabetes. Our dataset contains 768 records with 9 features, and the output is the probability of whether the patient has diabetes or not. Figure 3 illustrates the proposed intelligent health system for predicting diabetes in the cloud of health cities. The remote sensing of the patient's current status is transferred automatically into the intelligent healthcare system and stored in an EHR for further processing. It can be noticed from this figure that the patients data are processed in the cloud and trained according to pretrained models, and the required action could be activated automatically based on the results obtained from the cloud processing. Figure 4 shows the proposed diabetes prediction/monitoring system in smart health cities that is running in the cloud.  
The integration of cloud computing and Bluetooth-connected devices can enhance healthcare monitoring by providing remote and real-time data collection, storage, and analysis. With cloud computing, data collected from Bluetooth devices, such as glucose monitors, blood pressure cuffs, and activity trackers, can be transmitted and stored securely on remote servers for easy access by healthcare providers. This allows for more efficient and frequent monitoring of patients and can assist in the early detection and intervention of any health issues. Additionally, cloud computing can enable the use of advanced analytics and machine learning algorithms to identify patterns and make predictions, improving the overall management of chronic conditions, such as diabetes.  
Notifications in healthcare monitoring systems are set to trigger when certain thresholds are met. They are typically based on established clinical guidelines and are specific to the condition being monitored. For example, notifications may be sent when blood sugar levels exceed a certain level, indicating a need for immediate intervention. These thresholds are fixed, as they are based on the expert's knowledge and evidence-based research and are intended to provide objective measures for emergency response. The notifications are used to alert healthcare providers, who will then determine if an ambulance should be called.  
Figure 3: Cloud computing architecture related to diabetes healthcare.