# Predicting Stroke from Electronic Health Records

## TL;DR

This paper analyzes electronic health records to understand risk factors for stroke and benchmarks machine learning algorithms for stroke prediction, highlighting the inter-dependencies among risk factors.

## Contribution

It introduces an analysis of EHR data to study risk factor inter-dependencies and provides benchmark results for machine learning stroke prediction models.

## Key findings

- Identified key risk factors influencing stroke prediction.
- Benchmark performance of state-of-the-art algorithms on EHR data.
- Insights into inter-dependencies among risk factors.

## Abstract

Studies have identified various risk factors associated with the onset of stroke in an individual. Data mining techniques have been used to predict the occurrence of stroke based on these factors by using patients' medical records. However, there has been limited use of electronic health records to study the inter-dependency of different risk factors of stroke. In this paper, we perform an analysis of patients' electronic health records to identify the impact of risk factors on stroke prediction. We also provide benchmark performance of the state-of-art machine learning algorithms for predicting stroke using electronic health records.