Non-Invasive Method for Tuberculosis Exhaled Breath Classification Using Electronic Nose

Abstract

This research aims to apply an electronic nose (e-Nose) identifying TB through exhaled breath combined with machine learning methods. For the e-Nose system, participants breath normally into the e-Nose chamber. The e-Nose classified the exhaled breath into Tuberculosis Suspect (TBS) and Healthy Person (HP). The dataset was created by collecting directly after pulmonologist confirmed by using chest x-ray images. The machine learning methods were used to train and classify the exhaled breath. E-Nose models were created by using Support Vector Machine (SVM), Random Forest, and XGBoost. Based on experiments, the exhaled breath accuracy is 92 % for SVM, 88.24% for XGBoost, 94.87% for ANN, and 84.24% for the random forest.