  Stroke is widely considered as the second most common cause of mortality. The
adverse consequences of stroke have led to global interest and work for
improving the management and diagnosis of stroke. Various techniques for data
mining have been used globally for accurate prediction of occurrence of stroke
based on the risk factors that are associated with the electronic health care
records (EHRs) of the patients. In particular, EHRs routinely contain several
thousands of features and most of them are redundant and irrelevant that need
to be discarded to enhance the prediction accuracy. The choice of
feature-selection methods can help in improving the prediction accuracy of the
model and efficient data management of the archived input features. In this
paper, we systematically analyze the various features in EHR records for the
detection of stroke. We propose a novel rough-set based technique for ranking
the importance of the various EHR records in detecting stroke. Unlike the
conventional rough-set techniques, our proposed technique can be applied on any
dataset that comprises binary feature sets. We evaluated our proposed method in
a publicly available dataset of EHR, and concluded that age, average glucose
level, heart disease, and hypertension were the most essential attributes for
detecting stroke in patients. Furthermore, we benchmarked the proposed
technique with other popular feature-selection techniques. We obtained the best
performance in ranking the importance of individual features in detecting
stroke.
