Comprehensive interaction modeling with machine learning improves prediction of disease risk in the UK Biobank.

The discriminatory performance of survivalFM shows a positive trend and increasing gap to standard Cox regression with increasing sample sizes, although the gains often begin to plateau at the upper end of the sample size range. survivalFM improves prediction performance in a clinical cardiovascular risk prediction scenario To explore whether comprehensive interaction modeling via survivalFM could also refine well-established clinical risk prediction models, we conducted analyses in a clinical CVD risk prediction setting using predictors from the QRISK3 model 5 .

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