An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis
نویسندگان
چکیده
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.
منابع مشابه
An interpretable model for stroke prediction using rules and Bayesian analysis
We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. We introduce a Bayesian method for learning decision lists, a type of interpretable classifier, from data. We use the model to predict stroke in atrial fibrillation patients, and produce predictive models that are as interpretable as the current medical scoring systems that are in widesp...
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تاریخ انتشار 2013