Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease

Bannister, C. A. ORCID: https://orcid.org/0000-0001-8558-9480, Currie, C. J., Preece, A. ORCID: https://orcid.org/0000-0003-0349-9057 and Spasic, I. ORCID: https://orcid.org/0000-0002-8132-3885 2014. Automatic development of clinical prediction models with genetic programming: A case study in cardiovascular disease. Value in Health 17 (3) , A200-A201. 10.1016/j.jval.2014.03.1171

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Abstract

Objectives Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this study was to demonstrate the utility of genetic programming for the automatic development of clinical prediction models using cardiovascular disease as a case study. Methods We compared genetic programming and the commonly used Cox regression technique in the development of a cardiovascular risk score using data from the SMART study, a prospective cohort study designed to identify predictors of future cardiovascular events in patients with symptomatic cardiovascular disease. The primary outcome was any cardiovascular event, comprising cardiovascular death and non-fatal stroke and myocardial infarction. The predictive ability of the model was assessed in terms of discrimination and calibration. Results 3,873 patients were enrolled in the study 1996–2006, aged 19–82 years and with 460 cardiovascular events. The discrimination of both models was comparable; the C-index of the genetic programming model being smaller (0.65; 95% CI: 0.63–0.66) but not significantly different from that of the Cox regression model (0.71; 0.67–0.75). The calibration of both models was also comparable, indicating similar disagreement between observed and predicted risks. Conclusions Using empirical data, we demonstrated that a prediction model developed by the novel technique of genetic programming has a comparable predictive ability to that of Cox regression. The genetic programming model was more complicated but was developed in an automated fashion and did not require the expertise needed for survival analysis. Genetic programming seems a promising technique for the automated development of clinical prediction models for diagnostic and prognostic purposes.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Medicine MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General)
Publisher:	Wiley/Elsevier
ISSN:	1098-3015
Last Modified:	19 May 2023 01:19
URI:	https://orca.cardiff.ac.uk/id/eprint/60547

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