A Novel Machine Learning Approach for Tuberculosis Detection using Volatile Organic Compounds

Title

A Novel Machine Learning Approach for Tuberculosis Detection using Volatile Organic Compounds

Creator

Vishal, B.G.; George, Jossy; Chanti, S.

Description

For world health, better TB diagnosis is still absolutely necessary. Using VOC Atlas, this study assesses a few machine learning techniques for categorizing breath samples depending on volatile organic compound (VOC) profiles. We created a machine learning pipeline and tried out four different models: Random Forest, XGBoost, Multi-Layer Perceptron (MLP), and a 1D-Convolutional Neural Network (1D-CNN). There were 1,500 patient profiles in the dataset spanning three groups: healthy people, drug-sensitive TB cases, and multidrug-resistant TB cases. Using VOC biomarker patterns found in VOC Atlas and prior TB research, these profiles were developed. While XGBoost stood out by reaching 100% accuracy, our studies revealed that most models performed rather well. This implies that gradient boosting-based ensemble models can adequately grasp the complex patterns found in breath data. One major caveat is that we have not tested these models on real clinical breath samples to validate them. Testing these models with actual patient samples in clinical settings would be the next reasonable step. All told, this research provides a strong basis for creating non-invasive ways to detect illnesses. 2025 IEEE.

Source

Proceedings of the 4th International Conference on Intelligent Computing, Information and Control Systems, ICOIICS 2025;pp.1612-1618

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

1D-CNN; Breath Biopsy; Classification; Machine Learning; MDR-TB; Non-invasive Diagnosis; Random Forest; Tuberculosis (TB); Volatile Organic Compounds (VOCs); XGBoost

Coverage

Vishal B.G., CHRIST (Deemed to be University), Bangalore, India; George J., CHRIST (Deemed to be University), Bangalore, India; Chanti S., CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833159897-6;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICOIICS67115.2025.11390373

https://www.scopus.com/pages/publications/105036734426?origin=resultslist