# Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Source: [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:13:30.351Z Authors: Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan Published: 2015-11-05T08:01:05.000Z ## Abstract We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of simple, readily interpretable decision statements. We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists. It employs a novel prior structure to encourage sparsity. Our experiments show that Bayesian Rule Lists has predictive accuracy on par with the current top algorithms for prediction in machine learning. Our method is motivated by recent developments in personalized medicine, and can be used to produce highly accurate and interpretable medical scoring systems. We demonstrate this by producing an alternative to the CHADS$\_2$ score, actively used in clinical practice for estimating the risk of stroke in patients that have atrial fibrillation. Our model is as interpretable as CHADS$\_2$, but more accurate. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Outlier Explanation on CENSUS (Group 2)](): Outlier Explanation; CENSUS (Group 2); FUO: 1. - [Outlier Explanation on READMISSION (Outlier Group 1)](): Outlier Explanation; READMISSION (Outlier Group 1); FUO: 100. - [Outlier Explanation on READMISSION (Outlier Group 2)](): Outlier Explanation; READMISSION (Outlier Group 2); FUO: 0. - [Outlier Explanation on HEART DISEASE](): Outlier Explanation; HEART DISEASE; MDL: 656. - [Outlier Explanation on CENSUS (Group 3)](): Outlier Explanation; CENSUS (Group 3); FUO: 0. - [Outlier Explanation on CENSUS (Group 1)](): Outlier Explanation; CENSUS (Group 1); FUO: 10. ## Original sources and implementation [Original paper]() [PDF]()