The　negative　impact　of　stroke　in　society　has　led　to　concerted　efforts　to　improve　the　management　and　diagnosis　of　stroke.　With　an　increased　synergy　between　technology　and　medical　diagnosis,　caregivers　create　opportunities　for　better　patient　management　by　systematically　mining　and　archiving　the　patients’　medical　records.　Therefore,　it　is　vital　to　study　the　interdependency　of　these　risk　factors　in　patients’　health　records　and　understand　their　relative　contribution　to　stroke　prediction.　This　paper　systematically　analyzes　the　various　factors　in　electronic　health　records　for　effective　stroke　prediction.　Using　various　statistical　techniques　and　principal　component　analysis,　we　identify　the　most　important　factors　for　stroke　prediction.　We　conclude　that　age,　heart　disease,　average　glucose　level,　and　hypertension　are　the　most　important　factors　for　detecting　stroke　in　patients.　Furthermore,　a　perceptron　neural　network　using　these　four　attributes　provides　the　highest　accuracy　rate　and　lowest　miss　rate　compared　to　using　all　available　input　features　and　other　benchmarking　algorithms.　As　the　dataset　is　highly　imbalanced　concerning　the　occurrence　of　stroke,　we　report　our　results　on　a　balanced　dataset　created　via　sub-sampling　techniques.　©　2022　The　Author(s)