{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T10:23:16Z","timestamp":1770891796020,"version":"3.50.1"},"reference-count":62,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2022,9,29]]},"abstract":"<jats:p>Early screening for Alzheimer\u2019s disease (AD) is crucial for disease management, intervention, and healthcare resource accessibility. Medical assessments of AD diagnosis include the utilisation of biological markers (biomarkers), positron emission tomography (PET) scans, magnetic resonance imaging (MRI) images, and cerebrospinal fluid (CSF). These methods are resource intensive as well as physically invasive, whereas neuropsychological tests are fast, cost effective, and simple to administer for providing early AD diagnosis. However, neuropsychological assessments contain elements related to executive functions, memory, orientation, learning, judgment, and perceptual motor function (among others) that overlap, making it difficult to identify the key elements that trigger the progression of dementia or mild cognitive impairment (MCI). This research investigates the elements of the Functional Activities Questionnaire (FAQ) an early screening method using a data driven approach based on feature selection and classification. The aim is to determine the key items in the FAQ that may trigger AD advancement. To achieve the aim, real data observations of the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) project have been processed using the proposed data driven approach. The results derived by the machine learning techniques in the proposed approach on data subsets of the FAQ items with demographics show models with accuracy, sensitivity, and specificity all exceeding 90%. In addition, FAQ elements including Administration and Shopping related activities showed correlations with the progression class; these elements cover four out of the six Diagnostic and Statistical Manual\u2019s (DSM-5\u2019s) neurocognitive domains.<\/jats:p>","DOI":"10.3233\/idt-220054","type":"journal-article","created":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T15:48:30Z","timestamp":1655221710000},"page":"615-630","source":"Crossref","is-referenced-by-count":11,"title":["Detection of dementia progression from functional activities data using machine learning techniques"],"prefix":"10.1177","volume":"16","author":[{"given":"Fadi","family":"Thabtah","sequence":"first","affiliation":[{"name":"ASDTests, Auckland, New Zealand"}]},{"given":"Swan","family":"Ong","sequence":"additional","affiliation":[{"name":"Manukau Institute of Technology, Auckland, New Zealand"}]},{"given":"David","family":"Peebles","sequence":"additional","affiliation":[{"name":"Centre for Cognition and Neuroscience, Department of Psychology, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-220054_ref1","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp.2005.082867"},{"key":"10.3233\/IDT-220054_ref2","doi-asserted-by":"publisher","DOI":"10.1017\/s1041610297003943"},{"key":"10.3233\/IDT-220054_ref3","unstructured":"Alzheimer\u2019s New Zealand. (n.d.). What is dementia?; https:\/\/www.alzheimers.org.nz\/."},{"key":"10.3233\/IDT-220054_ref4","doi-asserted-by":"crossref","unstructured":"Sachdev PS, Blazer DG, Blacker D, Ganguli M. Classifying neurocognitive disorders: The DSM-5 approach. September 2014; https:\/\/pubmed.ncbi.nlm.nih.gov\/25266297\/.","DOI":"10.1038\/nrneurol.2014.181"},{"key":"10.3233\/IDT-220054_ref5","unstructured":"Deloitte. Dementia Economic Impact Report 2016. Alzheimers New Zealand; 2017. https:\/\/www.alzheimers.org.nz\/news\/dementia-economic-impact-rpcort-2016."},{"key":"10.3233\/IDT-220054_ref6","unstructured":"Suzuki E. Renewing priority for dementia: Where do we stand? OECD; 2018. http:\/\/www.oecd.org\/health\/health-systems\/Renewing-priority-for-dementia-Where-do-we-stand-2018.pdf."},{"key":"10.3233\/IDT-220054_ref7","unstructured":"NHS. Is there a cure for dementia? 2018. https:\/\/www.nhs.uk\/conditions\/dementia\/cure\/."},{"key":"10.3233\/IDT-220054_ref8","unstructured":"New Zealand framework for dementia care. Ministry of Health. 2013. https:\/\/bit.ly\/3p2nfth."},{"key":"10.3233\/IDT-220054_ref9","unstructured":"Medical tests. Alzheimer\u2019s Association. (n.d.). https:\/\/www.alz.org\/alzheimers-dementia\/diagnosis\/medical_tests."},{"key":"10.3233\/IDT-220054_ref10","doi-asserted-by":"crossref","unstructured":"Diagnostic and Statistical Manual of Mental Disorders 5th ed. American Psychiatric Association. 2013.","DOI":"10.1176\/appi.books.9780890425596"},{"key":"10.3233\/IDT-220054_ref11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1532-5415.2005.53221.x"},{"key":"10.3233\/IDT-220054_ref12","doi-asserted-by":"publisher","DOI":"10.1192\/bjp.140.6.566"},{"key":"10.3233\/IDT-220054_ref13","doi-asserted-by":"crossref","unstructured":"Folstein M, Folstein S, McHugh P. Mini-Mental State: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research; 1975; 12, 189-198.","DOI":"10.1016\/0022-3956(75)90026-6"},{"key":"10.3233\/IDT-220054_ref14","unstructured":"Mohs RC, Rosen WG, Davis KL. The Alzheimer\u2019s disease assessment scale: An instrument for assessing treatment efficacy. Psychopharmacology Bulletin; 1983; 19(3), 448-450. http:\/\/ci.nii.ac.jp\/naid\/10029841229\/en\/."},{"key":"10.3233\/IDT-220054_ref15","doi-asserted-by":"publisher","DOI":"10.1093\/geronj\/37.3.323"},{"key":"10.3233\/IDT-220054_ref16","doi-asserted-by":"publisher","DOI":"10.1080\/13854046.2015.1119312"},{"key":"10.3233\/IDT-220054_ref17","doi-asserted-by":"publisher","DOI":"10.2174\/1874350101811010142"},{"key":"10.3233\/IDT-220054_ref18","doi-asserted-by":"publisher","DOI":"10.1017\/s1041610218001692"},{"key":"10.3233\/IDT-220054_ref19","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.853294"},{"key":"10.3233\/IDT-220054_ref20","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.36553"},{"key":"10.3233\/IDT-220054_ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s12525-021-00475-2"},{"key":"10.3233\/IDT-220054_ref22","doi-asserted-by":"crossref","unstructured":"Thabtah F, Peebles D, Retzler J, Hathurusingha C. Dementia medical screening using mobile applications: A systematic review with a new mapping model. Journal of Biomedical Informatics, 111, 2020; 103573, ISSN 1532-0464.","DOI":"10.1016\/j.jbi.2020.103573"},{"key":"10.3233\/IDT-220054_ref23","doi-asserted-by":"publisher","DOI":"10.1186\/s12877-020-01926-9"},{"key":"10.3233\/IDT-220054_ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jagp.2019.11.003"},{"key":"10.3233\/IDT-220054_ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s13755-020-00114-8"},{"key":"10.3233\/IDT-220054_ref26","doi-asserted-by":"publisher","DOI":"10.2174\/156720501205150526115003"},{"key":"10.3233\/IDT-220054_ref27","unstructured":"Mayo A. Use of the Functional Activities Questionnaire in older adults with dementia. Alzheimer\u2019s Association, 2016; 13; https:\/\/www.alz.org\/careplanning\/downloads\/functional-activities-questionnaire.pdf."},{"key":"10.3233\/IDT-220054_ref28","unstructured":"Massachusetts Alzheimer\u2019s Disease Research Center (MA- DRC). (n.d.). Retrieved December 4, 2020; From https:\/\/www.madrc.org\/."},{"key":"10.3233\/IDT-220054_ref29","doi-asserted-by":"publisher","DOI":"10.1097\/WAD.0b013e3181e2fc84"},{"key":"10.3233\/IDT-220054_ref30","doi-asserted-by":"crossref","unstructured":"Beekly D, Ramos E, Lee W, Deitrich W, Jacka M, Wu J, Hubbard J, Koepsell T, Morris J, Kukull W. The National Alzheimer\u2019s Coordinating Center (NACC) database: The uniform data set. Alzheimer Disease and Associated Disorders; 2007; 21(3), 249-258.","DOI":"10.1097\/WAD.0b013e318142774e"},{"key":"10.3233\/IDT-220054_ref31","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387"},{"key":"10.3233\/IDT-220054_ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15314-3_30"},{"key":"10.3233\/IDT-220054_ref33","doi-asserted-by":"crossref","unstructured":"Vapnik V. An overview of statistical learning theory. IEEE Transactions on Neural Networks; 1999; 10(5).","DOI":"10.1109\/72.788640"},{"key":"10.3233\/IDT-220054_ref34","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/1850909"},{"key":"10.3233\/IDT-220054_ref35","doi-asserted-by":"crossref","unstructured":"Cortes C, Vapnik V. Support-vector networks. Machine Learning; 1995; 20(3). pp. 273-297.","DOI":"10.1007\/BF00994018"},{"key":"10.3233\/IDT-220054_ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbr.2018.02.017"},{"key":"10.3233\/IDT-220054_ref37","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"10.3233\/IDT-220054_ref38","doi-asserted-by":"publisher","DOI":"10.3390\/app7070651"},{"key":"10.3233\/IDT-220054_ref39","unstructured":"Rish I. An empirical study of the naive Bayes classifier. Proceedings of IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence; 2001."},{"key":"10.3233\/IDT-220054_ref40","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007465528199"},{"key":"10.3233\/IDT-220054_ref41","unstructured":"Quinlan J. Bagging, boosting, and C4.5. AAAI\/IAAI; 1996; 1. pp. 725-730."},{"key":"10.3233\/IDT-220054_ref42","unstructured":"Liaw A, Wiener M. Classification and regression by randomForest. R News; 2002; 2(3). pp. 18-22."},{"key":"10.3233\/IDT-220054_ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-007-9052-3"},{"key":"10.3233\/IDT-220054_ref44","doi-asserted-by":"publisher","DOI":"10.1016\/S1352-2310(97)00447-0"},{"key":"10.3233\/IDT-220054_ref45","unstructured":"Liu H, Setiono R. Chi2: feature selection and discretization of numeric attributes. Proceedings of the IEEE 7th International Conference on Tools with Artificial Intelligence; 1995; pp.\u00a0388-391. IEEE."},{"key":"10.3233\/IDT-220054_ref46","unstructured":"Mitchell T. Machine learning. McGraw-Hill; 1997."},{"key":"10.3233\/IDT-220054_ref47","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"10.3233\/IDT-220054_ref48","unstructured":"Hall M. Correlation-based feature selection for discrete and numeric class machine learning. 2000."},{"key":"10.3233\/IDT-220054_ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"10.3233\/IDT-220054_ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2017.06.251"},{"key":"10.3233\/IDT-220054_ref51","doi-asserted-by":"publisher","DOI":"10.3390\/app808137"},{"key":"10.3233\/IDT-220054_ref52","doi-asserted-by":"crossref","unstructured":"Brand L, Nichols K, Wang H, Huang H, Shen L. Predicting longitudinal outcomes of Alzheimer\u2019s disease via a tensor-based joint classification and regression model. Pacific Symposium on Biocomputing; 2020; 25 pp. 7-18.","DOI":"10.1142\/9789811215636_0002"},{"key":"10.3233\/IDT-220054_ref53","unstructured":"World Dementia Council. 2018. https:\/\/worlddementiacouncil.org\/sites\/default\/files\/2018-12\/Defeating20Dementia20Report.pdf."},{"key":"10.3233\/IDT-220054_ref54","unstructured":"Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). 2017. http:\/\/adni.loni.usc.edu\/about\/."},{"key":"10.3233\/IDT-220054_ref55","unstructured":"lzheimer\u2019s Disease Neuroimaging Initiative (ADNI). (n.d.). http:\/\/adni.loni.usc.edu\/wp-content\/uploads\/2010\/09\/ADNI_GeneralProceduresManual.pdf."},{"key":"10.3233\/IDT-220054_ref56","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"key":"10.3233\/IDT-220054_ref57","unstructured":"Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence (IJCAI); 1995."},{"key":"10.3233\/IDT-220054_ref58","doi-asserted-by":"publisher","DOI":"10.1002\/9781118646106.ch3"},{"key":"10.3233\/IDT-220054_ref59","doi-asserted-by":"crossref","unstructured":"Fix E, Hodges J. Discriminatory analysis: Nonparametric discrimination, consistency properties. Int Stat Rev; 1989.","DOI":"10.2307\/1403797"},{"key":"10.3233\/IDT-220054_ref60","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-106"},{"key":"10.3233\/IDT-220054_ref61","unstructured":"Harris Jr E. Information gain versus gain ratio: A study of split method biases. The MITRE Corporation; 2001."},{"key":"10.3233\/IDT-220054_ref62","unstructured":"Quinlan J. C4.5: Programs for machine learning. Morgan Kaufmann Publishers; 1993."}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-220054","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T10:10:35Z","timestamp":1741687835000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-220054"}},"subtitle":["For the Alzheimer\u2019s Disease Neuroimaging Initiative1"],"short-title":[],"issued":{"date-parts":[[2022,9,29]]},"references-count":62,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/idt-220054","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,29]]}}}