Fig. 1 | Scientific Reports

Fig. 1

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

Fig. 1The alternative text for this image may have been generated using AI.

Performance and interpretability of machine learning models applied to breathomics data. (A) Macro-averaged ROC curves for seven classification models using outer cross-validation. (B) SHAP summary plot showing the top 10 VOCs contributing to classification across asthma, bronchiectasis, and COPD. Each horizontal bar represents a VOC, identified by its PubChem CID number on the y-axis, with bar length and color indicating the magnitude and class-specific contribution. (Abbreviations: kNN = k-nearest neighbors, LR = logistic regression, NB = naïve Bayes, DT = decision tree, SVM = support vector machine, RF = random forest, and XGBoost = extreme gradient boosting.)

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