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Machine Learning-Driven E-Nose-Based Diabetes Detection: Sensor Selection and Feature Reduction Study.

Yavuz Selim Taspinar1

  • 1Department of Mechatronic Engineering, Selcuk University, Konya 42130, Türkiye.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study used electronic-nose (e-nose) breath analysis and machine learning to detect diabetes. The Artificial Neural Network (ANN) model achieved 100% accuracy in classifying individuals with or without diabetes.

Keywords:
electronic nosefeature analysisfeature reductionmachine learningrank analysis

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Data Science

Background:

  • Diabetes mellitus is a significant global health concern with increasing prevalence and severe long-term complications.
  • Early diagnosis of diabetes is crucial to prevent debilitating conditions such as cardiovascular disease, kidney failure, vision loss, and neurological disorders.

Purpose of the Study:

  • To classify individuals as diabetic or healthy using electronic-nose (e-nose) sensor data from breath samples.
  • To evaluate the performance of various machine learning models for diabetes detection.
  • To identify critical sensor features and optimize data processing for accurate classification.

Main Methods:

  • Breath samples from 1000 individuals were analyzed using an e-nose with six sensor features.
  • Machine learning algorithms including Artificial Neural Networks (ANN), Decision Trees (DT), Gradient Boosting (GB), Naive Bayes (NB), and AdaBoost (AB) were employed.
  • ANOVA and Information Gain analyses identified TGS2610 and TGS2611 sensors as critical; Principal Component Analysis (PCA) was used for dimensionality reduction.

Main Results:

  • The Artificial Neural Network (ANN) model demonstrated superior performance, achieving 100% classification accuracy.
  • AdaBoost (AB) and Gradient Boosting (GB) models achieved 99.8% accuracy, while Naive Bayes (NB) reached 97.6% accuracy.
  • Principal Component Analysis (PCA) effectively reduced data dimensionality, optimizing training and testing times without compromising accuracy.

Conclusions:

  • E-nose technology combined with machine learning offers a promising, data-driven approach for non-invasive diabetes detection.
  • The study highlights the effectiveness of specific sensors (TGS2610, TGS2611) and the importance of model selection (ANN) for accurate diagnosis.
  • Optimizing data size and sensor selection through techniques like PCA is vital for efficient and accurate e-nose-based diagnostic systems.