Identifying　patients　with　mild　cognitive　impairment　(MCI)　who　are　at　high　risk　of　progressing　to　Alzheimer＇s　disease　(AD)　is　crucial　for　early　treatment　of　AD.　However,　it　is　difficult　to　predict　the　cognitive　states　of　patients.　This　study　developed　an　extreme　learning　machine　(ELM)-based　grading　method　to　efficiently　fuse　multimodal　data　and　predict　MCI-to-AD　conversion.　First,　features　were　extracted　from　magnetic　resonance　(MR)　images,　and　useful　features　were　selected　using　a　feature　selection　method.　Second,　multiple　modalities　of　MCI　subjects,　including　MRI,　positron　emission　tomography,　cerebrospinal　fluid　biomarkers,　and　gene　data,　were　individually　graded　using　the　ELM　method.　Finally,　these　grading　scores　calculated　from　different　modalities　were　fed　into　a　classifier　to　discriminate　subjects　with　progressive　MCI　from　those　with　stable　MCI.　The　proposed　approach　has　been　validated　on　the　Alzheimer＇s　Disease　Neuroimaging　Initiative　(ADNI)　cohort,　and　an　accuracy　of　84.7%　was　achieved　for　an　AD　prediction　within　3　years.　Experiments　on　predicting　AD　conversion　from　MCI　within　different　periods　showed　similar　results　with　the　3-year　prediction.　The　experimental　results　demonstrate　that　the　proposed　approach　benefits　from　the　efficient　fusion　of　four　modalities,　resulting　in　an　accurate　prediction　of　MCI-to-AD　conversion.