Prediction of premature all-cause mortality: a prospective general population cohort study comparing machine-learning and standard epidemiological approaches

Weng, Stephen F; Vaz, Luis; Qureshi, Nadeem; Kai, Joe

Abstract

Background: Prognostic modelling using standard methods is well-established, particularly for predicting risk of single diseases. Machine-learning may offer potential to explore outcomes of even greater complexity, such as premature death. This study aimed to develop novel prediction algorithms using machine-learning, in addition to standard survival modelling, to predict premature all-cause mortality.

Methods: A prospective population cohort of 502,628 participants aged 40-69 years were recruited to the UK Biobank from 2006-2010 and followed-up until 2016. Participants were assessed on a range of demographic, biometric, clinical and lifestyle factors. Mortality data by ICD-10 were obtained from linkage to Office of National Statistics. Models were developed using deep learning, random forest and Cox regression. Calibration was assessed by comparing observed to predicted risks; and discrimination by area under the ‘receiver operating curve’ (AUC).

Findings: 14,418 deaths (2.9%) occurred over a total follow-up time of 3,508,454 person-years. A simple age and gender Cox model was the least predictive (AUC 0.689, 95% CI 0.681 – 0.699). A multivariate Cox regression model significantly improved discrimination by 6.2% (AUC 0.751, 95% CI 0.748 – 0.767). The application of machine-learning algorithms further improved discrimination by 3.2% using random forest (AUC 0.783, 95% CI 0.776 – 0.791) and 3.9% using deep learning (AUC 0.790, 95% CI 0.783 – 0.797). These ML algorithms improved discrimination by 9.4% and 10.1% respectively from a simple age and gender Cox regression model. Random forest and deep learning achieved similar levels of discrimination with no significant difference. Machine-learning algorithms were well-calibrated, while Cox regression models consistently over-predicted risk.

Conclusions: Machine-learning significantly improved accuracy of prediction of premature all-cause mortality in this middle-aged population, compared to standard methods. This study illustrates the value of machine-learning for risk prediction within a traditional epidemiological study design, and how this approach might be reported to assist scientific verification.

Citation

Weng, S. F., Vaz, L., Qureshi, N., & Kai, J. (2019). Prediction of premature all-cause mortality: a prospective general population cohort study comparing machine-learning and standard epidemiological approaches. PLoS ONE, 14(3), 1-22. https://doi.org/10.1371/journal.pone.0214365

• Journal Article Type: Article
• Acceptance Date: Mar 12, 2019
• Online Publication Date: Mar 27, 2019
• Publication Date: Mar 27, 2019
• Deposit Date: Mar 26, 2019
• Publicly Available Date: Mar 29, 2019
• Journal: PLOS ONE
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 3
• Article Number: e0214365
• Pages: 1-22
• DOI: https://doi.org/10.1371/journal.pone.0214365
• Keywords: premature all-cause mortality; machine-learning; risk prediction
• Public URL: https://nottingham-repository.worktribe.com/output/1669986
• Publisher URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0214365
• Contract Date: Mar 29, 2019