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The proposed approach is novel in a sense that it has higher specificity\/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.<\/jats:p>","DOI":"10.1155\/2014\/862307","type":"journal-article","created":{"date-parts":[[2014,9,9]],"date-time":"2014-09-09T17:02:12Z","timestamp":1410282132000},"page":"1-11","source":"Crossref","is-referenced-by-count":54,"title":["An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer\u2019s Disease: Classification Using Structural Features of Brain Images"],"prefix":"10.1155","volume":"2014","author":[{"given":"Saima","family":"Farhan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Lahore College for Women University, Jail Road, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4962-4546","authenticated-orcid":true,"given":"Muhammad Abuzar","family":"Fahiem","sequence":"additional","affiliation":[{"name":"Department 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