Long-term PM_2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation.

ABSTRACT: Clinical impact of fine particulate matter (PM_2.5) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM_2.5 for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837-0.853]) than existing traditional regression models using CHA₂DS₂-VASc (0.654 [0.646-0.661]), CHADS₂ (0.652 [0.646-0.657]), or HATCH (0.669 [0.661-0.676]) scores (each p < 0.001) fo

SUBMITTER: Kim IS 

PROVIDER: S-EPMC7530980 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature