Detection of Acetone as a Gas Biomarker for Diabetes Based on Gas Sensor Technology

TL;DR

This study develops a non-invasive, gas sensor-based system for detecting breath acetone levels, which are indicative of diabetes, using pattern recognition algorithms to improve early diagnosis accuracy.

Contribution

The paper introduces a novel acetone detection system utilizing gas sensors and machine learning models, enhancing non-invasive diabetes diagnosis methods.

Findings

Achieved 96% accuracy in acetone-ethanol gas recognition.

Achieved 97.5% accuracy in acetone-methanol gas recognition.

Achieved 90% accuracy in ternary gas mixture recognition.

Abstract

With the continuous development and improvement of medical services, there is a growing demand for improving diabetes diagnosis. Exhaled breath analysis, characterized by its speed, convenience, and non-invasive nature, is leading the trend in diagnostic development. Studies have shown that the acetone levels in the breath of diabetes patients are higher than normal, making acetone a basis for diabetes breath analysis. This provides a more readily accepted method for early diabetes prevention and monitoring. Addressing issues such as the invasive nature, disease transmission risks, and complexity of diabetes testing, this study aims to design a diabetes gas biomarker acetone detection system centered around a sensor array using gas sensors and pattern recognition algorithms. The research covers sensor selection, sensor preparation, circuit design, data acquisition and processing, and detection model establishment to accurately identify acetone. Titanium dioxide was chosen as the nano gas-sensitive material to prepare the acetone gas sensor, with data collection conducted using STM32. Filtering was applied to process the raw sensor data, followed by feature extraction using principal component analysis. A recognition model based on support vector machine algorithm was used for qualitative identification of gas samples, while a recognition model based on backpropagation neural network was employed for quantitative detection of gas sample concentrations. Experimental results demonstrated recognition accuracies of 96% and 97.5% for acetone-ethanol and acetone-methanol mixed gases, and 90% for ternary acetone, ethanol, and methanol mixed gases.