# Glucose Data Classification for Diabetic Patient Monitoring
## 2 Related Work  
This section presents some of the existing recent work related to e-health and monitoring glucose for diabetic patients. [PERSON] et al. [8] presented a survey in IoT-based health care technologies and reviewed the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. [PERSON] et al. [9] presented an intelligent communication architecture for ambient assisted living (AAL). The process of artificial intelligence was used to gather the information from several types of communication and showed the main intelligent algorithms included in the AAL system; furthermore, a software application was developed. Finally, they used several real measurements to validate their proposal operation. [PERSON] et al. [10] presented in progress e-health architecture using IoT for data acquisition, fog for data pre-processing and short-term storage, and cloud for data processing, analyze and long-term storage. This work also describes the main challenges to provide an e-health application with high availability, high performance, and accessibility, at low deployment and maintenance cost. [PERSON] et al. [11] proposed real-time glucose monitoring for an electrochemical sensor based on a wireless system. A simple sensor tag with low power and low-cost solutions were contained in the implanted glucose monitoring application. The accurate results provided by the system and its use of real-time application were the advantages. [PERSON] and [PERSON] [12] worked on development of a new blood glucose sensor. This sensor had the role of controlling and monitoring the level of glucose for diabetic patients. The real-time measurement of the blood glucose level was measured by a continuous glucose monitoring system (CGMS). This method used an alarm to indicate the level of the glycemic event as well as to improve the quality of life. [PERSON] [13] designed a system for glucose concentration prediction of diabetic patients to avoid hypoglycemia or hyperglycemia of serious complications. They used GlucoSim software to analyze the information of the patients. It was generated by the continuous glucose monitoring system (CGMS), which adopted a Kalman Filter (KF) to reduce noise. In [14] [PERSON] et al. presented a comprehensive survey on non-invasive/pain-free blood glucose monitoring methods from the five year period, 2012-2016. The devices manufactured or being manufactured for non-invasive monitoring were also compared in this paper. [PERSON] et al. [15] proposed a system architecture, Body Cloud, that integrates BSN services with a cloud computing infrastructure. This architecture supports the storage and management of sensor data streams and the processing (online and offline analysis) of the stored data using software services hosted in the cloud. In our paper, we propose an IoT application based patient monitoring system and diagnostic prediction tool for diabetic patients. Most people with diabetes die because they do not receive appropriate medical care at the right time. The development in the field of sensors and the Internet of Things helps to constantly monitor the health parameters of patients. For those reasons, we use a simple sensor with low power and low-cost solutions for the glucose monitoring application. The patient's data is updated in the cloud every day. The collected data is used by the doctor to monitor the patient's blood glucose variation visually and protect the patient's health from a critical condition. With the help of sensors, the current health parameters of patients are collected and forwarded to the cloud for storage. Each health dataset is analyzed and verified to determine precisely if the current values are within the normal range or not. An SMS is sent to notify the doctor in the case of any change in the vital signs. The critical data is then sent directly to the prediction system. The system will consider this data as trial data and predict whether or not the patient has a high risk of stroke.