Data　analyses　using　artificial　intelligence　(AI)　have　not　gained　popularity　in　social　work　as　much　as　other　disciplines.　To　demonstrate　its　use,　this　study　focused　on　Chinese　older　adults　with　neurodegenerative　diseases　(NDs)　to　(i)　develop　a　prediction　model　using　decision　tree　model　to　identify　factors　associated　with　depression　and　(ii)　compare　the　prediction　performance　of　decision　tree　model　with　that　of　logistic　regression　analysis.　Decision　tree　model　processing　involved　four　stages:　data　collection,　data　preparation,　model　development,　and　result　evaluation.　An　algorithm　named　Classification　and　Regression　Trees　(CARTs)　was　utilised　to　grow　the　decision　tree　by　Python　3.7.1.　The　performance　evaluation　was　based　on　accuracy,　sensitivity,　specificity　and　Goodness　index　(G).　Seven　factors　grew　the　decision　tree,　including　Instrumental　Activities　of　Daily　Living　(IADLs),　Mini-Mental　State　Examination　(MMSE),　Health　status,　Activity　of　Daily　Living　(ADL),　Gender,　Self-rated　health　change　and　Age.　When　compared　to　logistic　regression,　the　decision　tree　model　had　a　much　better　performance　in　depression　prediction.　Researchers,　practitioners　and　policymakers　need　to　focus　on　ways　to　decrease　the　vulnerability　of　depression　in　Chinese　older　adults　with　NDs.　Also,　the　decision　tree　model　can　be　applied　as　a　referral　to　other　physical　or　mental　diseases　prediction　and　analysis.