Machine learning algorithms for dementia prediction: A systematic review

• Autores: Guido Haro, Marco Lucas, Mendoza Rivera, Ricardo Dario, Lecca Rengifo, Maria Alexandra, Cruz Ulloa, Leydi Marisol, Huamanchumo Gordillo , Alexander Saul, Quispe Sanchez, Edward Steven
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/309
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/309; https://doi.org/10.48168/innosoft.s24.a309; https://purl.org/42411/s24/a309; https://n2t.net/ark:/42411/s24/a309
• Nivel de acceso: acceso abierto
• Materia: Algorithms; Dementia; Detection; Machine Learning; Algoritmos; Demencia; Detección

The following work addresses the identification of algorithms used in machine learning for the early detection of dementia or degenerative cognitive impairment, currently one of the main clinical and socioeconomic challenges of this century. It indicates the most relevant machine learning algorithms that, with their high reliability and effectiveness, are gaining ground in a much more technological world. The methodology used corresponds to the PRISMA declaration standards, using highly demanding research repositories such as SCOPUS, SCIELO, IEEE XPLORE, SAGE JOURNAL, and GOOGLE SCHOLAR, finding 15 works that met all established criteria. The results of the review in these works found many comparisons by academic study. Among the most widely used models are Random Forest and SVM, which have shown accuracies above 85% in multiple studies. The conclusions affirm the relevance of Machine Learning as a technological tool in the detection of dementia and its varieties, indicating opportunities for future research, particularly in more specific case studies where the use of technology is essential to assist humans.