{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T12:41:54Z","timestamp":1790772114141,"version":"4.1.0"},"reference-count":32,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T00:00:00Z","timestamp":1602547200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAD"],"published-print":{"date-parts":[[2020,10,13]]},"abstract":"<jats:p>Background: The widespread incidence and prevalence of Alzheimer\u2019s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. Objective: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). Methods: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. Results: Comparing the learners across selected combinations of MoCA scores and thresholds, Na\u00efve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). Conclusion: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.<\/jats:p>","DOI":"10.3233\/jad-191340","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T12:14:55Z","timestamp":1599221695000},"page":"1545-1558","source":"Crossref","is-referenced-by-count":21,"title":["Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment"],"prefix":"10.1177","volume":"77","author":[{"given":"Michael F.","family":"Bergeron","sequence":"first","affiliation":[{"name":"SIVOTEC Analytics, Boca Raton, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Landset","sequence":"additional","affiliation":[{"name":"Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianbo","family":"Zhou","sequence":"additional","affiliation":[{"name":"SJN Biomed LTD, Kunming, Yunnan, China"},{"name":"Center for Alzheimer\u2019s Research, Washington Institute of Clinical Research, Washington, DC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taghi M.","family":"Khoshgoftaar","sequence":"additional","affiliation":[{"name":"Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Neurology, Dehong People\u2019s Hospital, Dehong, Yunnan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinjie","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Neurology, the First Affiliated Hospital of Kunming Medical University, Wuhua District, Kunming, Yunnan Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianmei","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Neurology, the First Affiliated Hospital of Kunming Medical University, Wuhua District, Kunming, Yunnan Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Neurology, the First Affiliated Hospital of Kunming Medical University, Wuhua District, Kunming, Yunnan Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Wesson","family":"Ashford","sequence":"additional","affiliation":[{"name":"War-Related Illness and Injury Study Center, VA Palo Alto Health Care System, Palo Alto, CA, USA"},{"name":"Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JAD-191340_ref1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.jalz.2016.03.001","article-title":"2016 Alzheimer\u2019s disease facts and figures","volume":"12","author":"Alzheimer\u2019s Association","year":"2016","journal-title":"Alzheimers Dement"},{"key":"10.3233\/JAD-191340_ref2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1002\/hec.3962","article-title":"Effect of early-stage Alzheimer\u2019s disease on household financial outcomes","volume":"29","author":"Gresenz","year":"2019","journal-title":"Health Econ"},{"key":"10.3233\/JAD-191340_ref3","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1212\/WNL.0000000000008259","article-title":"Quality improvement in neurology: Mild cognitive impairment quality measurement set","volume":"93","author":"Foster","year":"2019","journal-title":"Neurology"},{"key":"10.3233\/JAD-191340_ref4","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1002\/gps.4626","article-title":"Cost effectiveness of using cognitive screening tests for detecting dementia and mild cognitive impairment in primary care","volume":"32","author":"Tong","year":"2017","journal-title":"Int J Geriatr Psychiatry"},{"key":"10.3233\/JAD-191340_ref5","doi-asserted-by":"crossref","first-page":"885","DOI":"10.3233\/JAD-2011-110950","article-title":"Measuring memory in large group settings using a continuous recognition test","volume":"27","author":"Ashford","year":"2011","journal-title":"J Alzheimers Dis"},{"key":"10.3233\/JAD-191340_ref6","doi-asserted-by":"crossref","first-page":"385","DOI":"10.3233\/JAD-190167","article-title":"A computerized continuous-recognition task for measurement of episodic memory","volume":"69","author":"Ashford","year":"2019","journal-title":"J Alzheimers Dis"},{"key":"10.3233\/JAD-191340_ref7","doi-asserted-by":"crossref","first-page":"277","DOI":"10.3233\/JAD-190165","article-title":"Episodic-memory performance in machine learning modeling for predicting cognitive health status classification","volume":"70","author":"Bergeron","year":"2019","journal-title":"J Alzheimers Dis"},{"key":"10.3233\/JAD-191340_ref8","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.3233\/JAD-181003","article-title":"The MemTrax test compared to the montreal cognitive assessment estimation of mild cognitive impairment","volume":"67","author":"van der Hoek","year":"2019","journal-title":"J Alzheimers Dis"},{"key":"10.3233\/JAD-191340_ref9","doi-asserted-by":"crossref","unstructured":"Falcone M , Yadav N , Poellabauer C , Flynn P (2013) Using isolated vowel sounds for classification of mild traumatic brain injury. 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