Automatic covariate selection in logistic models for chest pain diagnosis: A new approach

Abstract

A newly established method for optimizing logistic models via a minorizationmajorization procedure is applied to the problem of diagnosing acute coronary syndromes (ACS). The method provides a principled approach to the selection of covariates which would otherwise require the use of a suboptimal method owing to the size of the covariate set. A strategy for building models is proposed and two models optimized for performance and for simplicity are derived via ten-fold crossvalidation. These models confirm that a relatively small set of covariates including clinical and electrocardiographic features can be used successfully in this task.The performance of the models is comparable with previously published models using less principled selection methods. The models prove to be portable when tested on data gathered from three other sites. Whilst diagnostic accuracy and calibration diminishes slightly for these new settings, it remains satisfactory overall.The prospect of building predictive models that are as simple as possible for a required level of performance is valuable if data-driven decision aids are to gain wide acceptance in the clinical situation owing to the need to minimize the time taken to gather and enter data at the bedside.

Clinical Prediction Models

“…The Lasso and variants of such a method are promising techniques when prediction and parsimony are goals of predictive modelling. 179,434 Questions 11.1 Stepwise selection methods…”

A simple iterative algorithm for parsimonious binary kernel Fisher discrimination

“…Furthermore, during iteration one or more ω i may head towards zero which could affect the convergence toward the minimizer of the objective function. Our own experience here, in [28][29][30] and that of [25][26][27] suggests no practical problem. Indeed [27] shows for the penalized least-squares case with convex objective function that if ω (0) = 0 then, with probability one, no ω i achieves zero in a finite number of iterations so from a practical viewpoint there should be no difficulty.…”