{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:28:22Z","timestamp":1772252902932,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T00:00:00Z","timestamp":1581292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the past decade, many studies have been conducted to advance computer-aided systems for Alzheimer\u2019s disease (AD) diagnosis. Most of them have recently developed systems concentrated on extracting and combining features from MRI, PET, and CSF. For the most part, they have obtained very high performance. However, improving the performance of a classification problem is complicated, specifically when the model\u2019s accuracy or other performance measurements are higher than 90%. In this study, a novel methodology is proposed to address this problem, specifically in Alzheimer\u2019s disease diagnosis classification. This methodology is the first of its kind in the literature, based on the notion of replication on the feature space instead of the traditional sample space. Briefly, the main steps of the proposed method include extracting, embedding, and exploring the best subset of features. For feature extraction, we adopt VBM-SPM; for embedding features, a concatenation strategy is used on the features to ultimately create one feature vector for each subject. Principal component analysis is applied to extract new features, forming a low-dimensional compact space. A novel process is applied by replicating selected components, assessing the classification model, and repeating the replication until performance divergence or convergence. The proposed method aims to explore most significant features and highest-preforming model at the same time, to classify normal subjects from AD and mild cognitive impairment (MCI) patients. In each epoch, a small subset of candidate features is assessed by support vector machine (SVM) classifier. This repeating procedure is continued until the highest performance is achieved. Experimental results reveal the highest performance reported in the literature for this specific classification problem. We obtained a model with accuracies of 98.81%, 81.61%, and 81.40% for AD vs. normal control (NC), MCI vs. NC, and AD vs. MCI classification, respectively.<\/jats:p>","DOI":"10.3390\/s20030941","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T09:25:21Z","timestamp":1581413121000},"page":"941","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study\u2014Alzheimer\u2019s Disease Diagnosis"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4894-0291","authenticated-orcid":false,"given":"Hamid","family":"Akramifard","sequence":"first","affiliation":[{"name":". Faculty of Electrical and Computer Engineering, University of Tabriz, East Azerbaijan, Tabriz 51666-16471, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-0871","authenticated-orcid":false,"given":"MohammadAli","family":"Balafar","sequence":"additional","affiliation":[{"name":". Faculty of Electrical and Computer Engineering, University of Tabriz, East Azerbaijan, Tabriz 51666-16471, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"SeyedNaser","family":"Razavi","sequence":"additional","affiliation":[{"name":". Faculty of Electrical and Computer Engineering, University of Tabriz, East Azerbaijan, Tabriz 51666-16471, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abd Rahman","family":"Ramli","sequence":"additional","affiliation":[{"name":". Department of Computer and Communication Systems Engineering, University Putra Malaysia, UPM-Serdang 43400, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,10]]},"reference":[{"key":"ref_1","first-page":"131","article-title":"2012 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement","volume":"8","author":"Thies","year":"2012","journal-title":"J. Alzheimer\u2019s Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.neurobiolaging.2006.11.010","article-title":"Detection of prodromal Alzheimer\u2019s disease via pattern classification of magnetic resonance imaging","volume":"29","author":"Davatzikos","year":"2008","journal-title":"Neurobiol. 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