A baseline study of interpretable machine learning using GC-MS breath VOCs for classifying asthma, bronchiectasis, and COPD.

ABSTRACT: Accurate differentiation among asthma, bronchiectasis, and chronic obstructive pulmonary disease (COPD) remains a critical challenge due to overlapping clinical symptoms and limitations of conventional diagnostic tools. This study establishes a transparent, reproducible baseline using gas chromatography-mass spectrometry (GC-MS) data derived from exhaled breath to classify asthma, bronchiectasis, and COPD. Using a publicly available clinical dataset comprising 121 breath samples and 76 shared volatile organic compounds (VOCs), we evaluated seven supervised classifiers under nested cross-validation. Among the classifiers, XGBoost achieved the highest performance, with a mean accuracy of 95.83% and macro-averaged AUC of 0.998. To enhance clinical interpretability, we applied Shapley Additive

SUBMITTER: Ko EJ 

PROVIDER: S-EPMC12728165 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature