| Title: | Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study |
| Journal: | Stroke and Vascular Neurology |
| Published: | 27 Apr 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37105576/ |
| DOI: | https://doi.org/10.1136/svn-2023-002332 |
| URL: | https://svn.bmj.com/content/svnbmj/early/2023/04/26/svn-2023-002332.full.pdf |
Publication 11386
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Abstract
BACKGROUND: Previous prediction algorithms for cardiovascular diseases (CVD) were established using risk factors retrieved largely based on empirical clinical knowledge. This study sought to identify predictors among a comprehensive variable space, and then employ machine learning (ML) algorithms to develop a novel CVD risk prediction model.</p>
METHODS: From a longitudinal population-based cohort of UK Biobank, this study included 473 611 CVD-free participants aged between 37 and 73 years old. We implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident CVD. The model was validated through a leave-one-center-out cross-validation.</p>
RESULTS: During a median follow-up of 12.2 years, 31 466 participants developed CVD within 10 years after baseline visits. A novel UK Biobank CVD risk prediction (UKCRP) model was established that comprised 10 predictors including age, sex, medication of cholesterol and blood pressure, cholesterol ratio (total/high-density lipoprotein), systolic blood pressure, previous angina or heart disease, number of medications taken, cystatin C, chest pain and pack-years of smoking. Our model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.762±0.010 that outperformed multiple existing clinical models, and it was well-calibrated with a Brier Score of 0.057±0.006. Further, the UKCRP can obtain comparable performance for myocardial infarction (AUC 0.774±0.011) and ischaemic stroke (AUC 0.730±0.020), but inferior performance for haemorrhagic stroke (AUC 0.644±0.026).</p>
CONCLUSION: ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.</p>
8 Authors
- Jia You
- Yu Guo
- Ju-Jiao Kang
- Hui-Fu Wang
- Ming Yang
- Jian-Feng Feng
- Jin-Tai Yu
- Wei Cheng
1 Application
| Application ID | Title |
|---|---|
| 19542 | Identifying multi-level biomarkers and disease mechanisms for major mental disorders |
Enabling scientific discoveries that improve human health