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Related Experiment Video

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Noninvasive Total Cholesterol Level Measurement Using an E-Nose System and Machine Learning on Exhaled Breath

Anna Paleczek1, Justyna Grochala2, Dominik Grochala1

  • 1AGH University of Krakow, Faculty of Computer Science Electronics and Telecommunications, Institute of Electronics, al. A. Mickiewicza 30, Krakow 30-059, Poland.

ACS Sensors
|November 22, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel electronic-nose (e-nose) system using machine learning to noninvasively measure total cholesterol levels from breath samples. This breath analysis offers a promising alternative for monitoring cholesterol. Keywords: electronic-nose, machine learning, total cholesterol, noninvasive measurement, breath analysis.

Keywords:
E-nose systemLGBMRegressorexhaled breath analysisgas sensorsmachine learningnoninvasive measurementpredictive modelingtotal cholesterol level

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Machine Learning Applications

Background:

  • Current methods for measuring total cholesterol levels are invasive, often requiring blood draws.
  • There is a need for noninvasive, convenient, and accurate methods for cholesterol monitoring.
  • Exhaled breath contains volatile organic compounds that may correlate with physiological states.

Purpose of the Study:

  • To propose and evaluate the first electronic-nose (e-nose) system coupled with a machine learning algorithm for noninvasive total cholesterol measurement.
  • To assess the feasibility of using exhaled air samples for predicting cholesterol levels.

Main Methods:

  • A cohort of 151 participants provided breath samples.
  • Breath samples were analyzed using an e-nose equipped with various gas sensors (TGS1820, TGS2620, TGS2600, MQ3, Semeatech 7e4 NO2/H2S, SGX_NO2/H2S, K33, AL-03P/S).
  • The Light Gradient Boosting Machine Regressor (LGBMRegressor) algorithm was employed to predict total cholesterol levels.

Main Results:

  • Machine learning models achieved a Mean Absolute Percentage Error (MAPE) of 13.7% for the entire measurement range.
  • For the normal cholesterol range (≤200 mg/dL), the MAPE was significantly reduced to 8%.
  • The study demonstrates a correlation between exhaled breath composition and total cholesterol levels.

Conclusions:

  • It is feasible to develop a noninvasive device for measuring total cholesterol levels using exhaled air.
  • The proposed e-nose system combined with machine learning shows potential as a screening tool for cholesterol.
  • Further research can refine the system for improved accuracy and clinical application.