Abstract
BackgroundGenetic programming (GP) is an evolutionary computing methodology capable of identifying complex, non-linear patterns in large data sets. Despite the potential advantages of GP over more typical, frequentist statistical approach methods, its applications to survival analyses are rare, at best. The aim of this study was to determine the utility of GP for the automatic development of clinical prediction models.MethodsWe compared GP against the commonly used Cox regression technique in terms of the development and performance of a cardiovascular risk score using data from the SMART study, a prospective cohort study of patients with symptomatic cardiovascular disease. The composite endpoint was cardiovascular death, non-fatal stroke, and myocardial infarction. A total of 3,873 patients aged 19–82 years were enrolled in the study 1996–2006. The cohort was split 70:30 into derivation and validation sets. The derivation set was used for development of both GP and Cox regression models. These models were then used to predict the discrete hazards at t = 1, 3, and 5 years. The predictive ability of both models was evaluated in terms of their risk discrimination and calibration using the validation set.ResultsThe discrimination of both models was comparable. At time points t = 1, 3, and 5 years the C-index was 0.59, 0.69, 0.64 and 0.66, 0.70, 0.70 for the GP and Cox regression models respectively. At the same time points, the calibration of both models, which was assessed using calibration plots and the generalization of the Hosmer-Lemeshow test statistic, was also comparable, but with the Cox model being better calibrated to the validation data.ConclusionUsing empirical data, we demonstrated that a prediction model developed automatically by GP has predictive ability comparable to that of manually tuned Cox regression. The GP model was more complex, but it was developed in a fully automated way and comprised fewer covariates. Furthermore, it did not require the expertise normally needed for its derivation, thereby alleviating the knowledge elicitation bottleneck. Overall, GP demonstrated considerable potential as a method for the automated development of clinical prediction models for diagnostic and prognostic purposes.
Highlights
At the same time points, the calibration of both models, which was assessed using calibration plots and the generalization of the Hosmer-Lemeshow test statistic, was comparable, but with the Cox model being better calibrated to the validation data
We demonstrated that a prediction model developed automatically by Genetic programming (GP) has predictive ability comparable to that of manually tuned Cox regression
This study demonstrated that Cox regression and GP produced comparable results when evaluated on a common validation data set
Summary
All diagnostic and therapeutic actions aim to improve prognosis [1]. Physicians and health policy makers need to make predictions on the disease prognosis to support their decision making. We live in an era of evidence-based medicine, which is defined as "the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients" [2]. Evidence-based medicine applies scientific methods to clinical practice in order to improve the quality of patient care [3]. Patients themselves are involved in their care through shared decision making, where both physicians and patients actively participate in making choices about diagnostic tests and therapeutic interventions [1, 4]. The aim of this study was to determine the utility of GP for the automatic development of clinical prediction models
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