Background　and　objective:　This　paper　presents　an　automatic　computer-aided　diagnosis　(CAD)　system　based　on　feature　ranking　for　detection　of　Alzheimer＇s　disease　(AD)　using　structural　magnetic　resonance　imaging　(sMRI)　data.　Methods:　The　proposed　CAD　system　is　composed　of　four　systematic　stages.　First,　global　and　local　differences　in　the　gray　matter　(GM)　of　AD　patients　compared　to　the　GM　of　healthy　controls　(HCs)　are　analyzed　using　a　voxel-based　morphometry　technique.　The　aim　is　to　identify　significant　local　differences　in　the　volume　of　GM　as　volumes　of　interests　(VOIs).　Second,　the　voxel　intensity　values　of　the　VOIs　are　extracted　as　raw　features.　Third,　the　raw　features　are　ranked　using　a　seven-feature　ranking　method,　namely,　statistical　dependency　(SD),　mutual　information　(MI),　information　gain　(IG),　Pearson＇s　correlation　coefficient　(PCC),　t-test　score　(TS),　Fisher＇s　criterion　(FC),　and　the　Gini　index　(GI).　The　features　with　higher　scores　are　more　discriminative.　To　determine　the　number　of　top　features,　the　estimated　classification　error　based　on　training　set　made　up　of　the　AD　and　HC　groups　is　calculated,　with　the　vector　size　that　minimized　this　error　selected　as　the　top　discriminative　feature.　Fourth,　the　classification　is　performed　using　a　support　vector　machine　(SVM).　In　addition,　a　data　fusion　approach　among　feature　ranking　methods　is　introduced　to　improve　the　classification　performance.　Results:　The　proposed　method　is　evaluated　using　a　data-set　from　ADNI　(130　AD　and　130　HC)　with　10-fold　cross-validation.　The　classification　accuracy　of　the　proposed　automatic　system　for　the　diagnosis　of　AD　is　up　to　92.48%　using　the　sMRI　data.　Conclusions:　An　automatic　CAD　system　for　the　classification　of　AD　based　on　feature-ranking　method　and　classification　errors　is　proposed.　In　this　regard,　seven-feature　ranking　methods　(i.　e.,　SD,　MI,　IG,　PCC,　TS,　FC,　and　GI)　are　evaluated.　The　optimal　size　of　top　discriminative　features　is　determined　by　the　classification　error　estimation　in　the　training　phase.　The　experimental　results　indicate　that　the　performance　of　the　proposed　system　is　comparative　to　that　of　state-of-the-art　classification　models.　(C)　2016　Elsevier　Ireland　Ltd.　All　rights　reserved.