{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T03:49:11Z","timestamp":1792036151164,"version":"4.4.0"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer\u2019s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.<\/jats:p>","DOI":"10.3390\/diagnostics11112103","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:48:36Z","timestamp":1636922916000},"page":"2103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Improved Alzheimer\u2019s Disease Detection by MRI Using Multimodal Machine Learning Algorithms"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0603-2356","authenticated-orcid":false,"given":"Gopi","family":"Battineni","sequence":"first","affiliation":[{"name":"Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2585-9260","authenticated-orcid":false,"given":"Mohmmad Amran","family":"Hossain","sequence":"additional","affiliation":[{"name":"Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0818-306X","authenticated-orcid":false,"given":"Nalini","family":"Chintalapudi","sequence":"additional","affiliation":[{"name":"Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enea","family":"Traini","sequence":"additional","affiliation":[{"name":"Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Venkata Rao","family":"Dhulipalla","sequence":"additional","affiliation":[{"name":"The Research Centre of the ECE Department, V.R. Siddhartha Engineering College, Vijayawada 521002, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariappan","family":"Ramasamy","sequence":"additional","affiliation":[{"name":"The Research Centre of the ECE Department, V.R. Siddhartha Engineering College, Vijayawada 521002, Andhra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0555-1034","authenticated-orcid":false,"given":"Francesco","family":"Amenta","sequence":"additional","affiliation":[{"name":"Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2016). 2016 Alzheimer\u2019s disease facts and figures. 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