{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T06:42:10Z","timestamp":1787812930639,"version":"build-2784847793"},"reference-count":49,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2021,2,16]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>There is a need for more reliable diagnostic tools for the early detection of Alzheimer\u2019s disease (AD). This can be a challenge due to a number of factors and logistics making machine learning a viable option.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>In this paper, we present on a Support Vector Machine Leave-One-Out Recursive Feature Elimination and Cross Validation (SVM-RFE-LOO) algorithm for use in the early detection of AD and show how the SVM-RFE-LOO method can be used for both classification and prediction of AD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Data were analyzed on n\u200a=\u200a300 participants ( n\u200a=\u200a150 AD; n\u200a=\u200a150 cognitively normal controls). Serum samples were assayed via a multi-plex biomarker assay platform using electrochemiluminescence (ECL).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The SVM-RFE-LOO method reduced the number of features in the model from 21 to 16 biomarkers and achieved an area under the curve (AUC) of 0.980 with a sensitivity of 94.0% and a specificity of 93.3%. When the classification and prediction performance of SVM-RFE-LOO was compared to that of SVM and SVM-RFE, we found similar performance across the models; however, the SVM-RFE-LOO method utilized fewer markers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>We found that 1) the SVM-RFE-LOO is suitable for analyzing noisy high-throughput proteomic data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance. Our recursive feature elimination model can serve as a general model for biomarker discovery in other diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3233\/jad-201254","type":"journal-article","created":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T13:02:54Z","timestamp":1611061374000},"page":"1691-1700","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":39,"title":["Recursive Support Vector Machine Biomarker Selection for Alzheimer\u2019s Disease"],"prefix":"10.1177","volume":"79","author":[{"given":"Fan","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA"},{"name":"Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa","family":"Petersen","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA"},{"name":"Department of Family Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leigh","family":"Johnson","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Hall","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sid E.","family":"O\u2019Bryant","sequence":"additional","affiliation":[{"name":"Institute for Translational Research, Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,1,18]]},"reference":[{"key":"ref001","unstructured":"Alzheimer\u2019s Association, 2020 Alzheimer\u2019s Disease Facts and Figures, http:\/\/www.alz.org\/facts\/, Accessed 3 Dec 2020."},{"key":"ref002","unstructured":"The Gerontological Society of America Workgroup on Cognitive Impairment Detection and Earlier Diagnosis: Report and Recommendations, 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