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.　stroke.　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.