Article

Can we use machine learning to discover risk factors? Testing the proof of principle using data on >11,000 predictors and mortality in the UK Biobank

2021-05-10

Abstract excerpt

<h4>Background</h4> We present a simple and fast hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. <h4>Methods</h4> Mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using SHAP values. Cox models controlled for f...

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Literature Corpus work: fdc9d6bb-f0fe-50d9-88ba-f28eab87c7b2
DOI: 10.1101/2021.05.07.21256791

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