{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T01:51:26Z","timestamp":1781747486242,"version":"3.54.5"},"posted":{"date-parts":[[2026,6,15]]},"group-title":"Neurology","reference-count":21,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Alzheimer\u2019s disease (AD) is a critical neurological disorder that causes the brain to shrink and leads to the eventual death of brain cells, adversely affecting a person\u2019s ability to function. AD is a fast-growing disease in the United States and was the fifth leading cause of death among Americans 65 years of age or older in 2023. In the United States, 6.9 million people aged 65 or older were diagnosed with AD, along with a high rate of undiagnosed patients. Thus, the objective of our study is to develop a real data-driven predictive model to identify a patient with AD based on eight risk factors: Age, Gender, ADAS-Cog13, Entorhinal, Fusiform, Intracranial Volume (ICV), Amyloid-Beta, and Tau Protein, with a high degree of accuracy. The quality of the model was evaluated using well-established and sophisticated statistical measures: the area under the receiver operating characteristic curve, calibration plot, Hosmer\u2013Lemeshow goodness-of-fit test, and K-fold cross-validation. If a patient is given information on the above risk factors, our proposed binary logistic regression model can classify the patient as having AD or not with at least 98% accuracy.<\/jats:p>","DOI":"10.64898\/2026.06.06.26355081","type":"posted-content","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T01:20:31Z","timestamp":1781572831000},"source":"Crossref","is-referenced-by-count":0,"title":["Data Driven Stochastic Model for Detecting Patients with Alzheimer\u2019s Disease"],"prefix":"10.64898","author":[{"given":"Dilmi","family":"Abeywardana","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, Loyola University Maryland, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris P.","family":"Tsokos","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of South Florida, Tampa, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026061718051175000_2026.06.06.26355081v1.1","doi-asserted-by":"crossref","unstructured":"Sokratis G Papageorgiou Konstantina G Yiannopoulou . 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Harvard Health Publishing, 1 2022."},{"key":"2026061718051175000_2026.06.06.26355081v1.8","doi-asserted-by":"publisher","DOI":"10.1186\/s12929-019-0524-y"},{"key":"2026061718051175000_2026.06.06.26355081v1.9","doi-asserted-by":"publisher","DOI":"10.3390\/MOLECULES25245789"},{"key":"2026061718051175000_2026.06.06.26355081v1.10","doi-asserted-by":"crossref","unstructured":"Moreno-Gonzalez I Morales R Baglietto-Vargas D Sanchez-Varo R. Risk factors for alzheimer\u2019s disease. Front. Aging Neurosci., 12(124), 2020.","DOI":"10.3389\/fnagi.2020.00124"},{"issue":"20","key":"2026061718051175000_2026.06.06.26355081v1.11","first-page":"680","article-title":"Modifiable risk factors for alzheimer disease and related dementias among adults aged 45 years \u2014 united states, 2019","volume":"71","year":"2022","journal-title":"Front. 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