{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:08:48Z","timestamp":1792130928064,"version":"4.4.0"},"reference-count":71,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Consejer\u00eda de Vicepresidencia Primera y de Obras P\u00fablicas, Infraestructuras, Transporte y Movilidad del Cabildo de Gran Canaria","award":["23\/2021"],"award-info":[{"award-number":["23\/2021"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The proportion of older people will soon include nearly a quarter of the world population. This leads to an increased prevalence of non-communicable diseases such as Alzheimer\u2019s disease (AD), a progressive neurodegenerative disorder and the most common dementia. mild cognitive impairment (MCI) can be considered its prodromal stage. The early diagnosis of AD is a huge issue. We face it by solving these classification tasks: MCI-AD and cognitively normal (CN)-MCI-AD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An intelligent computing system has been developed and implemented to face both challenges. A non-neural preprocessing module was followed by a processing one based on a hybrid and ontogenetic neural architecture, the modular hybrid growing neural gas (MyGNG). The MyGNG is hierarchically organized, with a growing neural gas (GNG) for clustering followed by a perceptron for labeling. For each task, 495 and 819 patients from the Alzheimer\u2019s disease neuroimaging initiative (ADNI) database were used, respectively, each with 211 characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Encouraging results have been obtained in the MCI-AD classification task, reaching values of area under the curve (AUC) of 0.96 and sensitivity of 0.91, whereas 0.86 and 0.9 in CN-MCI-AD. Furthermore, a comparative study with popular machine learning (ML) models was also performed for each of these tasks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The MyGNG proved to be a better computational solution than the other ML methods analyzed. Also, it had a similar performance to other deep learning schemes with neuroimaging. Our findings suggest that our proposal may be an interesting computing solution for the early diagnosis of AD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076241284349","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T06:28:10Z","timestamp":1728368890000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Toward an intelligent computing system for the early diagnosis of Alzheimer\u2019s disease based on the modular hybrid growing neural gas"],"prefix":"10.1177","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5709-2274","authenticated-orcid":false,"given":"Ylermi","family":"Cabrera-Le\u00f3n","sequence":"first","affiliation":[{"name":"Instituto Universitario de Cibern\u00e9tica, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2135-6095","authenticated-orcid":false,"given":"Pablo","family":"Fern\u00e1ndez-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Instituto Universitario de Cibern\u00e9tica, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patricio","family":"Garc\u00eda B\u00e1ez","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Inform\u00e1tica y de Sistemas, Universidad de La Laguna , San Crist\u00f3bal de La Laguna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0983-2228","authenticated-orcid":false,"given":"Konrad","family":"Kluwak","sequence":"additional","affiliation":[{"name":"Department of Control Systems and Mechatronics, Wroc\u0142aw University of Science and Technology, Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Luis","family":"Navarro-Mesa","sequence":"additional","affiliation":[{"name":"Instituto Universitario para el Desarrollo Tecnol\u00f3gico y la Innovaci\u00f3n en Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmen Paz","family":"Su\u00e1rez-Araujo","sequence":"additional","affiliation":[{"name":"Instituto Universitario de Cibern\u00e9tica, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative *","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"bibr1-20552076241284349","unstructured":"Gauthier S, Webster C, Servaes S, Morais JA, Rosa-Neto P. World Alzheimer Report 2022 \u2013 Life after diagnosis: Navigating treatment, care and support. 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Transfer learning for Alzheimer\u2019s disease detection on MRI images. In\n                      2019 IEEE international conference on industry 4.0, Artificial intelligence, and communications technology (IAICT)\n                      , Bali, Indonesia, 01\u201303 July 2019, pp.133\u2013138. New York City, NY, USA: IEEE.","DOI":"10.1109\/ICIAICT.2019.8784845"},{"key":"bibr15-20552076241284349","doi-asserted-by":"publisher","DOI":"10.2741\/4606"},{"key":"bibr16-20552076241284349","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3174033"},{"key":"bibr17-20552076241284349","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12020270"},{"key":"bibr18-20552076241284349","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2021.637386"},{"key":"bibr19-20552076241284349","doi-asserted-by":"crossref","unstructured":"Cabrera-Le\u00f3n Y, B\u00e1ez PG, Ruiz-Alzola J, et al. Classification of mild cognitive impairment stages using machine learning methods. 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