Cargando…

Machine learning for the prediction of cognitive impairment in older adults

OBJECTIVE: The purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm. METHODS: The complete data of 2,226 participants aged 60–80 years were extracted from the 2011–2014 National Health and Nutrit...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Wanyue, Zeng, Li, Yuan, Shiqi, Shang, Yaru, Zhuang, Weisheng, Chen, Zhuoming, Lyu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10172509/
https://www.ncbi.nlm.nih.gov/pubmed/37179565
http://dx.doi.org/10.3389/fnins.2023.1158141
_version_ 1785039631612379136
author Li, Wanyue
Zeng, Li
Yuan, Shiqi
Shang, Yaru
Zhuang, Weisheng
Chen, Zhuoming
Lyu, Jun
author_facet Li, Wanyue
Zeng, Li
Yuan, Shiqi
Shang, Yaru
Zhuang, Weisheng
Chen, Zhuoming
Lyu, Jun
author_sort Li, Wanyue
collection PubMed
description OBJECTIVE: The purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm. METHODS: The complete data of 2,226 participants aged 60–80 years were extracted from the 2011–2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application. RESULTS: The study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application. CONCLUSIONS: ML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly.
format Online
Article
Text
id pubmed-10172509
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-101725092023-05-12 Machine learning for the prediction of cognitive impairment in older adults Li, Wanyue Zeng, Li Yuan, Shiqi Shang, Yaru Zhuang, Weisheng Chen, Zhuoming Lyu, Jun Front Neurosci Neuroscience OBJECTIVE: The purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm. METHODS: The complete data of 2,226 participants aged 60–80 years were extracted from the 2011–2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application. RESULTS: The study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application. CONCLUSIONS: ML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly. Frontiers Media S.A. 2023-04-27 /pmc/articles/PMC10172509/ /pubmed/37179565 http://dx.doi.org/10.3389/fnins.2023.1158141 Text en Copyright © 2023 Li, Zeng, Yuan, Shang, Zhuang, Chen and Lyu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Wanyue
Zeng, Li
Yuan, Shiqi
Shang, Yaru
Zhuang, Weisheng
Chen, Zhuoming
Lyu, Jun
Machine learning for the prediction of cognitive impairment in older adults
title Machine learning for the prediction of cognitive impairment in older adults
title_full Machine learning for the prediction of cognitive impairment in older adults
title_fullStr Machine learning for the prediction of cognitive impairment in older adults
title_full_unstemmed Machine learning for the prediction of cognitive impairment in older adults
title_short Machine learning for the prediction of cognitive impairment in older adults
title_sort machine learning for the prediction of cognitive impairment in older adults
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10172509/
https://www.ncbi.nlm.nih.gov/pubmed/37179565
http://dx.doi.org/10.3389/fnins.2023.1158141
work_keys_str_mv AT liwanyue machinelearningforthepredictionofcognitiveimpairmentinolderadults
AT zengli machinelearningforthepredictionofcognitiveimpairmentinolderadults
AT yuanshiqi machinelearningforthepredictionofcognitiveimpairmentinolderadults
AT shangyaru machinelearningforthepredictionofcognitiveimpairmentinolderadults
AT zhuangweisheng machinelearningforthepredictionofcognitiveimpairmentinolderadults
AT chenzhuoming machinelearningforthepredictionofcognitiveimpairmentinolderadults
AT lyujun machinelearningforthepredictionofcognitiveimpairmentinolderadults