An interpretable stroke prediction model using rules and Bayesian analysis

Published

Conference Paper

We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. We introduce a generative model called the Bayesian List Machine for fitting decision lists, a type of interpretable classifier, to data. We use the model to predict stroke in atrial fibrillation patients, and produce predictive models that are simple enough to be understood by patients yet significantly outperform the medical scoring systems currently in use.

Duke Authors

Cited Authors

  • Letham, B; Rudin, C; McCormick, TH; Madigan, D

Published Date

  • January 1, 2013

Published In

  • Aaai Workshop Technical Report

Volume / Issue

  • WS-13-17 /

Start / End Page

  • 65 - 67

International Standard Book Number 13 (ISBN-13)

  • 9781577356288

Citation Source

  • Scopus