Abstract
Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.
Highlights
IntroductionDiabetes is a long-term metabolic disorder in which the blood glucose (BG) level varies and is caused by either insufficient insulin production in the body (Type 1 diabetes, T1D) or by the body’s inability to utilize its produced insulin (Type 2 diabetes, T2D) [1,2,3]
Diabetes mellitus, more commonly referred as diabetes, has become a worldwide epidemic.Diabetes is a long-term metabolic disorder in which the blood glucose (BG) level varies and is caused by either insufficient insulin production in the body (Type 1 diabetes, T1D) or by the body’s inability to utilize its produced insulin (Type 2 diabetes, T2D) [1,2,3]
To the best of our knowledge, the present study is the first focusing on system integration of Bluetooth Low Energy (BLE)-based sensor device, smartphone, real-time data processing and machine learning-based methods to predict diabetes and BG levels
Summary
Diabetes is a long-term metabolic disorder in which the blood glucose (BG) level varies and is caused by either insufficient insulin production in the body (Type 1 diabetes, T1D) or by the body’s inability to utilize its produced insulin (Type 2 diabetes, T2D) [1,2,3]. The diagnosis of both T1D and T2D has increased, but the rise has been greater for T2D, which accounts for 90–95% of all cases of diabetes and is a growing epidemic that places a severe burden on healthcare systems, especially in developing countries [4].
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