| Title: | Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction |
| Journal: | Patterns |
| Published: | 4 Oct 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34950898/ |
| DOI: | https://doi.org/10.1016/j.patter.2021.100364 |
| URL: | http://www.cell.com/article/S2666389921002269/pdf |
Publication 10408
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Abstract
Current cardiovascular risk assessment tools use a small number of predictors. Here, we study how machine learning might: (1) enable principled selection from a large multimodal set of candidate variables and (2) improve prediction of incident coronary artery disease (CAD) events. An elastic net-based Cox model (ML4HEN-COX) trained and evaluated in 173,274 UK Biobank participants selected 51 predictors from 13,782 candidates. Beyond most traditional risk factors, ML4HEN-COX selected a polygenic score, waist circumference, socioeconomic deprivation, and several hematologic indices. A more than 30-fold gradient in 10-year risk estimates was noted across ML4HEN-COX quintiles, ranging from 0.25% to 7.8%. ML4HEN-COX improved discrimination of incident CAD (C-statistic = 0.796) compared with the Framingham risk score, pooled cohort equations, and QRISK3 (range 0.754-0.761). This approach to variable selection and model assessment is readily generalizable to a broad range of complex datasets and disease endpoints.</p>
10 Authors
- Saaket Agrawal
- Marcus D.R. Klarqvist
- Connor Emdin
- Aniruddh P. Patel
- Manish D. Paranjpe
- Patrick T. Ellinor
- Anthony Philippakis
- Kenney Ng
- Puneet Batra
- Amit V. Khera
Enabling scientific discoveries that improve human health