In　recent　years,　mild　cognitive　impairment　(MCI)　has　attracted　significant　attention　as　an　indicator　of　high　risk　for　Alzheimer＇s　disease　(AD),　and　the　diagnosis　of　MCI　can　alert　patient　to　carry　out　appropriate　strategies　to　prevent　AD.　To　avoid　subjectivity　in　diagnosis,　we　propose　an　ontology　driven　decision　support　method　which　is　an　automated　procedure　for　diagnosing　MCI　through　magnetic　resonance　imaging　(MRI).　In　this　approach,　we　encode　specialized　MRI　knowledge　into　an　ontology　and　construct　a　rule　set　using　machine　learning　algorithms.　Then　we　apply　these　two　parts　in　conjunction　with　reasoning　engine　to　automatically　distinguish　MCI　patients　from　normal　controls　(NC).　The　rule　set　is　trained　by　MRI　data　of　187　MCI　patients　and　177　normal　controls　selected　from　Alzheimer＇s　Disease　Neuroimaging　Initiative　(ADNI)　using　C4.5　algorithm.　By　using　a　10-fold　cross　validation,　we　prove　that　the　performance　of　C4.5　with　80.2%　sensitivity　is　better　than　other　algorithms,　such　as　support　vector　machine　(SVM),　Bayesian　network　(BN)　and　back　propagation　(BP)　neural　networks,　and　C4.5　is　suitable　for　the　construction　of　reasoning　rules.　Meanwhile,　the　evaluation　results　suggest　that　our　approach　would　be　useful　to　assist　physicians　efficiently　in　real　clinical　diagnosis　for　the　disease　of　MCI.　(c)　2014　Elsevier　Ireland　Ltd.　All　rights　reserved.