Problems　with　inconsistency　and　incompleteness　are　widely　found　in　rule-based　decision　support　systems.　These　problems　often　impact　the　accuracy　and　usability　of　rule-based　decision　support　systems.　The　present　work　focuses　on　an　advanced　rule-based　decision　support　system,　namely　the　extended　belief-rule-based　(EBRB)　system,　and　proposes　the　consistency　analysis-based　rule　activation　(CABRA)　method　to　overcome　the　above　two　problems　simultaneously.　However,　two　challenges　must　be　discussed　and　addressed　for　the　EBRB　system.　First,　rather　than　using　activated　weights,　suitable　activated　rules　must　be　redefined　to　better　analyze　the　problems　of　inconsistency　and　incompleteness.　Second,　suitable　activated　rules　must　be　selected　without　having　to　depend　on　subjective　information.　Therefore,　the　proposed　CABRA　method　uses　the　set　of　consistent　rules　as　an　activation　framework　to　define　suitable　activated　rules　before　calculating　their　activated　weights,　and　utilizes　the　CCR　model　as　a　selection　model　to　select　suitable　activated　rules　from　the　set　of　consistent　rules.　As　such,　by　embedding　the　CABRA　method,　the　EBRB　system　can　overcome　the　problems　of　inconsistency　and　incompleteness.　Three　case　studies　demonstrate　how　the　use　of　the　CABRA　method　improves　the　accuracy　and　rule　activation　rate　of　the　EBRB　system,　which　is　further　confirmed　by　comparisons　with　the　results　of　other　existing　studies.　In　addition　to　the　work　performed　in　the　EBRB　system,　the　CABRA　method　is　treated　as　a　generic　rule　activation　method　that　can　be　available　for　other　rule-based　decision　support　systems.　©　2018　Elsevier　Inc.