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

## TL;DR

This study shows that an e-nose with machine learning can detect diabetes with high accuracy using breath samples, making diagnosis faster and non-invasive.

## Contribution

The study introduces optimized sensor selection and dimensionality reduction techniques for e-nose-based diabetes detection using machine learning.

## Key findings

- The ANN model achieved 100% accuracy in diabetes classification using e-nose data.
- TGS2610 and TGS2611 sensors were identified as most discriminative for diabetes detection.
- PCA reduced data size without compromising accuracy, improving model efficiency.

## Abstract

What are the main findings?
The Artificial Neural Network (ANN) model achieved the best performance, reaching 100% accuracy in diabetes classification using e-nose data.Feature selection via ANOVA and Information Gain identified TGS2610 and TGS2611 sensors as the most discriminative for diabetes detection.

The Artificial Neural Network (ANN) model achieved the best performance, reaching 100% accuracy in diabetes classification using e-nose data.

Feature selection via ANOVA and Information Gain identified TGS2610 and TGS2611 sensors as the most discriminative for diabetes detection.

What is the implication of the main finding?
Optimized sensor selection and dimensionality reduction enable faster and more efficient model training without compromising accuracy.The proposed e-nose-based machine learning framework supports the development of non-invasive, practical, and clinically applicable diagnostic tools for diabetes.

Optimized sensor selection and dimensionality reduction enable faster and more efficient model training without compromising accuracy.

The proposed e-nose-based machine learning framework supports the development of non-invasive, practical, and clinically applicable diagnostic tools for diabetes.

Diabetes is a major global health problem, with a rapidly increasing prevalence and long-term health complications in both developed and developing countries. If not diagnosed early, it can lead to cardiovascular diseases, kidney failure, vision loss, and nervous system disorders. This study aimed to classify individuals with diabetes or healthy individuals using e-nose sensor data obtained from breath samples taken from 1000 individuals. Six sensor features and one class feature were used in the analysis. Machine learning methods included Artificial Neural Networks (ANN), Decision Trees (DT), Gradient Boosting (GB), Naive Bayes (NB), and AdaBoost (AB). ANOVA and Information Gain analyses were conducted to determine the effectiveness of the sensor data, and the TGS2610 and TGS2611 sensors were found to be critical for classification. Principal Component Analysis (PCA) reduced data size and saved processing time. Experimental results showed that the ANN model provided the most successful classification, with 100% accuracy. AB and GB achieved 99.8% accuracy, while NB achieved 97.6% accuracy. Dimensionality reduction using PCA optimized training and testing times without loss of accuracy. The study presents a data-driven approach to e-nose-based diabetes detection, demonstrates the comparative performance of the models, and highlights the importance of sensor selection and data size optimization.