Article

Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank

2022-10-30

Abstract excerpt

We employed machine learning (ML) approaches to evaluate 2,199 clinical features and disease phenotypes available in the UK Biobank as predictors for Atrial Fibrillation (AF) risk. After quality control, 99 features were selected for analysis in 21,279 prospective AF cases and equal number of controls. Different ML methods were employed, including LightGBM, XGBoost, Random Forest (RF), Deep Neural Network (DNN),)...

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Literature Corpus work: 37d0e5af-b524-5722-b99f-2522ba04152b
DOI: 10.1101/2022.10.28.22281669

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