Multimodal Spirometry Dataset for Quality Assessment: Tabular Parameters and Flow-Volume Loop Images

Each image is named as {Id}.png (e.g., 1.png, 100.png, 2336.png), directly corresponding to the Id column in the tabular file. Linkage: The Id column in the CSV file and the image filenames establish a one-to-one mapping between tabular records and flow-volume loop images. Annotations: The acceptability of spirometric maneuvers (adequate/inadequate) was labeled by expert physicians according to ATS/ERS 2005 guidelines and updated technical recommendations by Stanojevic et al. Class Distribution: Approximately balanced — 52.9% adequate (class 1, n=1,464) and 47.1% inadequate (class 0, n=1,304).

Missing Data: The data quality score is 70.6%, with notable missing rates in certain features (e.g., FEV1, FVC, and FEF values exceeding 90% missingness in some subsets). Missing values are encoded as "Na" in the CSV file. Usage: This dataset was used to train and evaluate multiple classification models including tree-based ensemble methods for tabular data, an EfficientNet-B2-based deep learning model for image data, and a late fusion architecture integrating both modalities.

Explainability analyses were conducted using SHAP, LIME, and Grad-CAM. Ethical Approval: Protocol No: 2024-GOKAEK-248_18.09.2024_148, Ethics Committee of Yozgat Bozok University. Associated Article: This dataset is associated with a manuscript currently under review.

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