{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T14:59:24Z","timestamp":1789570764002,"version":"build-2803163510"},"reference-count":13,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T00:00:00Z","timestamp":1612742400000},"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":["J Geriatr Psychiatry Neurol"],"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>Our aim was to develop a machine learning algorithm based only on non-invasively clinic collectable predictors, for the accurate diagnosis of these disorders.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>This is an ongoing prospective cohort study ( ClinicalTrials.gov identifier NCT number NCT04448340) of 78 PDD and 62 DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. We used predictors such as clinico-demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B). We investigated logistic regression, K-Nearest Neighbors (K-NNs) Support Vector Machine (SVM), Na\u00efve Bayes classifier, and Ensemble Model for their ability to predict successfully PDD or DLB diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The K-NN classification model had an accuracy 91.2% of overall cases based on 15 best clinical and cognitive scores achieving 96.42% sensitivity and 81% specificity on discriminating between DLB and PDD. The binomial logistic regression classification model achieved an accuracy of 87.5% based on 15 best features, showing 93.93% sensitivity and 87% specificity. The SVM classification model had an accuracy 84.6% of overall cases based on 15 best features achieving 90.62% sensitivity and 78.58% specificity. A model created on Na\u00efve Bayes classification had 82.05% accuracy, 93.10% sensitivity and 74.41% specificity. Finally, an Ensemble model, synthesized by the individual ones, achieved 89.74% accuracy, 93.75% sensitivity and 85.73% specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Machine learning method predicted with high accuracy, sensitivity and specificity PDD or DLB diagnosis based on non-invasively and easily in-the-clinic and neuropsychological tests.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0891988721993556","type":"journal-article","created":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T08:04:36Z","timestamp":1612771476000},"page":"317-320","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":27,"title":["A Novel Machine Learning Algorithm Predicts Dementia With Lewy Bodies Versus Parkinson\u2019s Disease Dementia Based on Clinical and Neuropsychological Scores"],"prefix":"10.1177","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3006-8711","authenticated-orcid":false,"given":"Anastasia","family":"Bougea","sequence":"first","affiliation":[{"name":"First Department of Neurology, National and Kapodistrian University, Aiginition Hospital, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Efthymia","family":"Efthymiopoulou","sequence":"additional","affiliation":[{"name":"First Department of Neurology, National and Kapodistrian University, Aiginition Hospital, Athens, Greece"},{"name":"Maison Sofos Elderly Care Unit, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1743-9648","authenticated-orcid":false,"given":"Ioanna","family":"Spanou","sequence":"additional","affiliation":[{"name":"251 General Air-Force Hospital, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Zikos","sequence":"additional","affiliation":[{"name":"251 General Air-Force Hospital, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"key":"bibr1-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1007\/s11910-018-0835-5"},{"key":"bibr2-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000004058"},{"key":"bibr3-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1007\/s00415-018-8992-7"},{"key":"bibr4-0891988721993556","doi-asserted-by":"publisher","DOI":"10.3233\/JPD-181367"},{"key":"bibr5-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1016\/j.parkreldis.2019.02.028"},{"key":"bibr6-0891988721993556","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.01097"},{"key":"bibr7-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1016\/j.trsl.2018.01.001"},{"key":"bibr8-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1002\/mds.21507"},{"key":"bibr9-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1002\/mds.22007"},{"key":"bibr10-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0213430"},{"key":"bibr11-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1016\/j.trci.2019.10.006"},{"key":"bibr12-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2011.2182616"},{"key":"bibr13-0891988721993556","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2471093"}],"container-title":["Journal of Geriatric Psychiatry and Neurology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0891988721993556","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/0891988721993556","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0891988721993556","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:16:27Z","timestamp":1777500987000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0891988721993556"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,8]]},"references-count":13,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["10.1177\/0891988721993556"],"URL":"https:\/\/doi.org\/10.1177\/0891988721993556","relation":{},"ISSN":["0891-9887","1552-5708"],"issn-type":[{"value":"0891-9887","type":"print"},{"value":"1552-5708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,8]]}}}