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oai:data.mendeley.com/ywpvkxnt2z.2 2026-03-11T11:40:57Z

ulutaş, hasan Multimodal Spirometry Dataset for Quality Assessment: Tabular Parameters and Flow-Volume Loop Images Mendeley Data This dataset accompanies the study titled "Explainable Machine Learning for Quality Assessment of Spirometry Tests: A Multimodal Framework." It comprises 2,768 spirometry records collected retrospectively from a tertiary care hospital (Yozgat Bozok University, Turkey) between January 2020 and October 2024. Dataset Structure The dataset consists of two components linked by a unique patient identifier (Id): spirometry_tabular_data.csv — A structured tabular file containing 2,768 records with 44 columns. The first column (Id) serves as the unique patient identifier that maps each row to its corresponding flow-volume loop image. The remaining columns include demographic parameters (Gender, Age, Height, Weight), environmental variables (Temperature, Barometric Pressure), spirometric measurements (FEV1, FVC, FEF25-75%, PEF, FET100%, FIVC, FIV1, FEF/FIF50, Vol Extrap) with their PRED, BEST, and %PRED values, and a binary target label (class: 1 = adequate, 0 = inadequate). flow_volume_loop_images/ — A folder containing 2,768 PNG images of flow-volume loops. 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. The full citation and DOI of the related article will be added upon publication. Artificial Intelligence Medical Informatics Respiratory System Clinical Decision Support System kızılkaya, hafize aydın, cihan şahin, muhammet emin korkmaz, murat Dataset doi:10.17632/ywpvkxnt2z.2 oai:data.mendeley.com/ywpvkxnt2z.2 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/ywpvkxnt2z 2026-03-11T11:40:57Z