Yudha Anggit Jiwantoro, Ni Putu Dian Ayu Anggraeni, Ninik Nurhidayah, I Gusti Ayu Sri Puja Warnis Wijayanti, Cembun Cembun

Effectiveness of Non-Invasive Sensor-Based Tools for Blood Glucose Detection

Introduction

Effectiveness of non-invasive sensor-based tools for blood glucose detection. Evaluate non-invasive sensor-based tools for blood glucose detection. An Arduino-based device offers comfortable diabetes monitoring with 83.3% accuracy, a promising alternative.

Abstract

Background: Monitoring blood glucose levels is one of the main pillars of diabetes management to prevent complications and reduce the risk of morbidity and mortality. Today's blood glucose monitoring is a non-invasive method that offers speed, accuracy, and painless convenience. Referring to this need, this study aims to demonstrate the effectiveness of non-invasive sensor-based detection devices in checking blood glucose levels in order to provide a more comfortable and efficient alternative for diabetes patients. Methods: This study developed a non-invasive glucometer using the latest and smaller version of Arduino Uno and tested it on 20 samples, consisting of 10 diabetes mellitus patients and 10 with normal blood glucose. The test was carried out by comparing the measurement results from the non-invasive device and the standard GCU Easy Touch 3-in-1 device to determine the accuracy of the device. The tool-testing method uses sensitivity, specificity, and accuracy. Results: This non-invasive measuring tool is more effective when used to measure patients with diabetes mellitus. This device shows an error rate of 9.21%, a sensitivity of 80%, and a specificity of 50%. Meanwhile, the overall measurement accuracy, calculated at 83.3%, reinforces the tool's effectiveness in providing reliable results. Conclusion: This device has the potential to be a convenient and painless method of blood glucose monitoring for diabetic patients. However, further development is needed to improve the development of machine learning-based algorithms to process sensor data so that tools can identify unique patterns from each individual and provide more accurate results.

Review

This study addresses a critical need in diabetes management: the development of non-invasive, convenient, and accurate methods for blood glucose monitoring. The current reliance on invasive methods poses challenges for patient adherence and comfort, making the pursuit of non-invasive alternatives highly significant. The authors aim to demonstrate the effectiveness of a novel non-invasive sensor-based detection device, proposing a potentially more comfortable and efficient solution for individuals managing diabetes. The premise of providing a painless and quick monitoring tool for a widespread chronic condition like diabetes is undeniably valuable. The methodology involved developing a non-invasive glucometer utilizing an Arduino Uno and testing it against a standard GCU Easy Touch 3-in-1 device. A small sample size of 20 individuals, comprising 10 diabetes mellitus patients and 10 healthy controls, was used for evaluation. Key metrics reported include an error rate of 9.21%, sensitivity of 80%, specificity of 50%, and an overall accuracy of 83.3%. While the overall accuracy appears reasonable, the notably low specificity of 50% is a significant concern, suggesting a high rate of false positives or negatives when distinguishing between diabetic and non-diabetic individuals. Furthermore, the abstract lacks crucial details regarding the specific type of non-invasive sensor employed, which is fundamental to understanding the device's operational principles and potential limitations. The authors conclude that the device holds potential as a convenient and painless monitoring method for diabetic patients and rightly call for further development, particularly in refining machine learning-based algorithms to enhance accuracy and identify individual patterns. However, the current findings are severely limited by the extremely small sample size, which precludes any robust generalization or clinical applicability. Future work must involve significantly larger and more diverse patient cohorts, provide explicit details about the underlying sensor technology, and focus on substantially improving the specificity to ensure reliable differentiation of blood glucose states. Comprehensive validation against established laboratory methods, beyond a single portable device, would also be essential to establish the device's true efficacy and safety for clinical use.

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