Please use this identifier to cite or link to this item: http://hdl.handle.net/10637/13935

A machine learning approach to design an efficient selective screening of mild cognitive impairment


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Title: A machine learning approach to design an efficient selective screening of mild cognitive impairment
Authors : Muñoz Almaraz, Francisco Javier
Climent Catalá, María Teresa
Guerrero Masiá, María Dolores
Moreno Royo, Lucrecia
Pardo Albiach, Juan
Keywords: Sistema nervioso - Degeneración - Diagnóstico - Modelos matemáticos.System nervous - Degeneration - Diagnosis - Mathematical models.Sistema nervioso - Enfermedades - Diagnóstico - Modelos matemáticos.Nervous system - Diseases - Diagnosis - Mathematical models.
Publisher: MyJoVE Corporation
Citation: Muñoz-Almaraz, F. J., Climent, M. T., Guerrero, M. D., Moreno, L. & Pardo, J. (2020). A machine learning approach to design an efficient selective screening of mild cognitive impairment. Journal of Visualized Experiments (JoVE), i. 155, art. e59649 (11 jan.). DOI: https://doi.org/10.3791/59649
Abstract: Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further. This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.
Description: Este artículo se encuentra disponible en la siguiente URL: https://www.jove.com/es/t/59649/a-machine-learning-approach-to-design-an-efficient-selective
URI: http://hdl.handle.net/10637/13935
Rights : http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
ISSN: 1940-087X (Electrónico)
Issue Date: 11-Jan-2020
Center : Universidad Cardenal Herrera-CEU
Appears in Collections:Dpto. Matemáticas, Física y Ciencias Tecnológicas





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