Witnessing　the　growth　of　inaccurate　data　with　uncertainty　and　ambiguity,　the　belief　rule　base　system　is　attracting　increasing　research　interest　because　of　its　usage　in　decision-making　problems　as　a　disjunctive　paradigm　particularly　when　there　is　incomplete　information.　In　this　paper,　we　devise　a　novel　method　that　employs　fuzzy　association　rules　to　optimize　disjunctive　belief　rule　base　(DBRB)　systems　regarding　incomplete　information　in　decision-making　and　achieve　a　significant　performance　improvement　compared　to　the　existing　ones.　First,　we　devise　a　method　for　constructing　a　fuzzy　representation　base　from　history　data　by　using　the　triangular　membership　functions　to　blur　history　information.　Then,　a　method　is　proposed　to　construct　association　rules　based　on　the　fuzzy　representation　base,　in　which　the　association　rules　are　found　regarding　the　missing　input　information　by　carrying　out　reasoning　to　complete　the　data　and　then　making　decisions　on　the　data　filled　with　DBRB　system.　Lastly,　we　evaluate　the　effectiveness　of　our　proposed　method　against　the　public　test　data　set,　demonstrating　that　our　method　outperforms　other　baselines　for　decision-making　with　incomplete　information.　(C)　2023　Published　by　Elsevier　Inc.