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Selection of a stroke risk model based on transcranial Doppler ultrasound velocity

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  • S. Mukhopadhyay
  • I. Das
  • K. Das

Abstract

Increased transcranial Doppler ultrasound (TCD) velocity is an indicator of cerebral infarction in children with sickle cell disease (SCD). In this article, the parallel genetic algorithm (PGA) is used to select a stroke risk model with TCD velocity as the response variable. Development of such a stroke risk model leads to the identification of children with SCD who are at a higher risk of stroke and their treatment in the early stages. Using blood velocity data from SCD patients, it is shown that the PGA is an easy-to-use computationally variable selection tool. The results of the PGA are also compared with those obtained from the stochastic search variable selection method, the Dantzig selector and conventional techniques such as stepwise selection and best subset selection.

Suggested Citation

  • S. Mukhopadhyay & I. Das & K. Das, 2012. "Selection of a stroke risk model based on transcranial Doppler ultrasound velocity," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(12), pages 2699-2712, August.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2699-2712
    DOI: 10.1080/02664763.2012.725463
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    Cited by:

    1. Weihua Zhao & Riquan Zhang & Yazhao Lv & Jicai Liu, 2014. "Variable selection for varying dispersion beta regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 95-108, January.

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