{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:53:31Z","timestamp":1764690811084,"version":"3.46.0"},"reference-count":55,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013409","name":"Sistema General de Regal\u00edas (SGR) de Colombia","doi-asserted-by":"publisher","award":["BPIN 2020000100449"],"award-info":[{"award-number":["BPIN 2020000100449"]}],"id":[{"id":"10.13039\/501100013409","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Alzheimer\u2019s disease (AD) accounts for ~70% of global dementia cases, with projections estimating 139 million affected individuals by 2050. This increasing burden highlights the urgent need for accessible, cost-effective diagnostic tools, particularly in low- and middle-income countries (LMICs). Traditional neuropsychological assessments, while effective, are resource-intensive and time-consuming. Methods: A total of 760 older adults (394 [51.8%] with AD) were recruited and neuropsychologically evaluated at the Instituto Colombiano de Neuropedagog\u00eda (ICN) in collaboration with Universidad del Norte (UN), Barranquilla. Machine learning (ML) algorithms were trained on a screening protocol incorporating demographic data and neuropsychological measures assessing memory, language, executive function, and praxis. Model performance was determined using 10-fold cross-validation. Variable importance analyses identified key predictors to develop optimized, abbreviated ML-based protocols. Metrics of compactness, cohesion, and separation further quantified diagnostic differentiation performance. Results: The eXtreme Gradient Boosting (xgbTree) algorithm achieved the highest diagnostic accuracy (91%) with the full protocol. Five ML-optimized screening protocols were also developed. The most efficient, the ICN-UN battery (including MMSE, Rey\u2013Osterrieth Complex Figure recall, Rey Auditory Verbal Learning, Lawton &amp; Brody Scale, and FAST), maintained strong diagnostic performance while reducing screening time from over four hours to under 25 min. Conclusions: The ML-optimized ICN-UN protocol offers a rapid, accurate, and scalable AD screening solution for LMICs. While promising for clinical adoption and earlier detection, further validation in diverse populations is recommended.<\/jats:p>","DOI":"10.3390\/diagnostics15233045","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:31:46Z","timestamp":1764689506000},"page":"3045","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer\u2019s Disease Diagnosis"],"prefix":"10.3390","volume":"15","author":[{"given":"Ernesto","family":"Barcel\u00f3","sequence":"first","affiliation":[{"name":"Instituto Colombiano de Neuropedagog\u00eda, Barranquilla 080020, Colombia"},{"name":"Department of Health Sciences, Universidad de La Costa, Barranquilla 080002, Colombia"},{"name":"Grupo Internacional de Investigaci\u00f3n Neuro-Conductual (GIINCO), Universidad de La Costa, Barranquilla 080002, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4828-9766","authenticated-orcid":false,"given":"Duban","family":"Romero","sequence":"additional","affiliation":[{"name":"Instituto Colombiano de Neuropedagog\u00eda, Barranquilla 080020, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ricardo","family":"Allegri","sequence":"additional","affiliation":[{"name":"Institute for Neurological Research FLENI, Monta\u00f1eses 2325, Buenos Aires C1428AQK, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eliana","family":"Meza","sequence":"additional","affiliation":[{"name":"Instituto Colombiano de Neuropedagog\u00eda, Barranquilla 080020, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9662-2925","authenticated-orcid":false,"given":"Mar\u00eda I.","family":"Mosquera-Heredia","sequence":"additional","affiliation":[{"name":"Department of Medicine, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7807-1242","authenticated-orcid":false,"given":"Oscar M.","family":"Vidal","sequence":"additional","affiliation":[{"name":"Department of Medicine, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos","family":"Silvera-Redondo","sequence":"additional","affiliation":[{"name":"Department of Medicine, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mauricio","family":"Arcos-Burgos","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Psiquiatr\u00eda (GIPSI), Departamento de Psiquiatr\u00eda, Instituto de Investigaciones M\u00e9dicas, Facultad de Medicina, Universidad de Antioquia, Medell\u00edn 050010, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pilar","family":"Garavito-Galofre","sequence":"additional","affiliation":[{"name":"Department of Medicine, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3146-7899","authenticated-orcid":false,"given":"Jorge I.","family":"V\u00e9lez","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Universidad del Norte, Barranquilla 081007, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2025, November 17). 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