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Background Breathlessness is a troublesome and prevalent symptom in the population, but knowledge of related factors is scarce. The aim of this study was to identify the factors most strongly associated with breathlessness in the general population and to describe the shapes of the associations between the main factors and breathlessness. Methods A cross-sectional analysis was carried out of the multicentre population-based Swedish CArdioPulmonary bioImage Study (SCAPIS) of adults aged 50 to 64 years. Breathlessness was defined as a modified Medical Research Council breathlessness rating ⩾2. The machine learning algorithm extreme gradient boosting (XGBoost) was used to classify participants as either breathless or nonbreathless using 449 factors, including physiological measurements, blood samples, computed tomography cardiac and lung measurements, lifestyle, health conditions and socioeconomics. The strength of the associations between the factors and breathlessness were measured by

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