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. 2023 Oct 10;12(20):6439.
doi: 10.3390/jcm12206439.

DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath

Ritu Kapur  1 , Yashwant Kumar  1 , Swati Sharma  1 , Vedant Rastogi  1 , Shivani Sharma  1 , Vikrant Kanwar  2 , Tarun Sharma  2 , Arnav Bhavsar  1 , Varun Dutt  1
Affiliations

DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath

Ritu Kapur et al. J Clin Med. .

Abstract

Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring.

Keywords: bio-markers; blood glucose monitoring; diabetes; diabetes test; digital health devices; exhaled breath analysis; non-invasive; volatile organic compounds.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Breath sample collection and analysis device arrangement.
Figure 2
Figure 2
Block diagram of our diabetes detection system.
Figure 3
Figure 3
Web interface for entering details and sensor’s response to a breath sample. (a) Personal Details Interface. (b) Body Vitals Interface. (c) TGS2603 Sensor's voltage response to an input breath sample. Segments AB and CD represent the sensor's switching state; Segment BC represents the sensor's stable ON state.
Figure 4
Figure 4
Diabetes report viewing web-interface. Note: The conclusions in blue represent normal readings, while those in red represent the abnormal readings.
Figure 5
Figure 5
SHAP plot representing the impact of features on classification decision.
See this image and copyright information in PMC

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