Observational studyBMC medical informatics and decision making2025
Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation parameters: an observational study.
Observational study in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
backgroundThe integration of machine learning (ML) algorithms enables the detection of diffusion abnormalities-related respiratory changes in individuals with normal body mass index (BMI), overweight, and obesity based on BMI and pulmonary ventilation parameters. We evaluated the effectiveness of various supervised ML algorithms and identified the optimal configurations for these applications.
methodsWe conducted a retrospective analysis of data from 440 individuals who underwent pulmonary function tests between January 1, 2021, and April 1, 2024. This cohort consisted of 287 individuals with normal diffusion capacity (DN) and 153 with diffusion abnormalities (DA). We employed statistical comparisons (e.g., independent samples t-test and Chi-square test) to analyze demographic characteristics and spirometry results. Piecewise regression evaluated the correlation between BMI and carbon monoxide diffusing capacity (DL
resultsOur findings revealed that individuals in the DA group demonstrated lower PEF and DL
conclusionThe results indicate that for individuals with a BMI between 18.5 kg/m² and 40 kg/m², diffusion capacity improves with increasing BMI. Conversely, diffusion capacity decreases for those with a BMI of 40 kg/m² or higher. This study underscores the potential of combining BMI and pulmonary ventilation parameters with ML algorithms as a practical approach to diagnosing diffusion abnormalities across normal-weight, overweight, and obese categories, particularly in contexts utilizing portable spirometers.
trial registrationNot applicable.
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