English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Stroke is widely considered as the second most common cause of mortality. Theadverse consequences of stroke have led to global interest and work forimproving the management and diagnosis of stroke. Various techniques for datamining have been used globally for accurate prediction of occurrence of strokebased on the risk factors that are associated with the electronic health carerecords (EHRs) of the patients. In particular, EHRs routinely contain severalthousands of features and most of them are redundant and irrelevant that needto be discarded to enhance the prediction accuracy. The choice offeature-selection methods can help in improving the prediction accuracy of themodel and efficient data management of the archived input features. In thispaper, we systematically analyze the various features in EHR records for thedetection of stroke. We propose a novel rough-set based technique for rankingthe importance of the various EHR records in detecting stroke. Unlike theconventional rough-set techniques, our proposed technique can be applied on anydataset that comprises binary feature sets. We evaluated our proposed method ina publicly available dataset of EHR, and concluded that age, average glucoselevel, heart disease, and hypertension were the most essential attributes fordetecting stroke in patients. Furthermore, we benchmarked the proposedtechnique with other popular feature-selection techniques. We obtained the bestperformance in ranking the importance of individual features in detectingstroke.

Identifying Stroke Indicators Using Rough Sets