Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

Abstract

We aim to produce predictive models that are not only accurate, but are also\ninterpretable to human experts. Our models are decision lists, which consist of\na series of if...then... statements (e.g., if high blood pressure, then stroke)\nthat discretize a high-dimensional, multivariate feature space into a series of\nsimple, readily interpretable decision statements. We introduce a generative\nmodel called Bayesian Rule Lists that yields a posterior distribution over\npossible decision lists. It employs a novel prior structure to encourage\nsparsity. Our experiments show that Bayesian Rule Lists has predictive accuracy\non par with the current top algorithms for prediction in machine learning. Our\nmethod is motivated by recent developments in personalized medicine, and can be\nused to produce highly accurate and interpretable medical scoring systems. We\ndemonstrate this by producing an alternative to the CHADS$_2$ score, actively\nused in clinical practice for estimating the risk of stroke in patients that\nhave atrial fibrillation. Our model is as interpretable as CHADS$_2$, but more\naccurate.\n

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Affiliated Institutions

• Columbia University US
• University of Washington US
• Massachusetts Institute of Technology US

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