Clostridium　difficile　infection　is　one　of　the　major　patient　safety　concerns　in　hospitals　worldwide.　Clostridium　difficile　infection　can　have　high　economic　burden　to　patients,　hospitals,　and　government.　Limited　work　has　been　done　in　the　area　of　predictive　modeling.　In　this　article,　A　new　predictive　model　based　on　Gaussian　mixture　model　and　Dempster-Shafter　theory　is　proposed　to　predict　Clostridium　difficile　infection　incidence　in　hospitals.　First,　the　Gaussian　mixture　model　and　expectation-maximization　algorithms　are　used　to　generate　explicit　probability　criteria　of　risk　factors　based　on　the　given　data.　Second,　Dempster-Shafter　theory　is　used　to　predict　the　Clostridium　difficile　infection　incidence　based　on　the　generated　probability　criteria　that　have　different　beliefs　attributing　to　their　different　credits.　The　main　procedure　includes　(1)　generate　the　probability　criteria　model　using　Gaussian　mixture　model　and　expectation-maximization　algorithm;　(2)　determine　the　credit　of　the　probability　criteria;　(3)　generate　the　basic　probability　assignment;　(4)　discount　the　evidences;　(5)　aggregate　the　evidences　using　Dempster　combining　rule;　(6)　predict　Clostridium　difficile　infection　incidence　using　pignistic　probability　transformation.　Results　show　that　the　model　has　a　higher　accuracy　than　an　existing　model.　The　proposed　model　can　generate　the　criteria　ratings　of　risk　factors　automatically,　which　would　potentially　prevent　the　imprecision　caused　by　the　subjective　judgement　of　experts.　The　proposed　model　can　assist　risk　managers　and　hospital　administrators　in　the　prediction　and　control　of　Clostridium　difficile　infection　incidence　with　optimizing　their　resources.