Prediction of future cognitive impairment among the community elderly: A machine-learning based approach View Full Text


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Article Info

DATE

2019-12

AUTHORS

Kyoung-Sae Na

ABSTRACT

The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging, genetic, and cerebrospinal measurements show promising results, high costs and invasiveness hinder their widespread use. Predicting cognitive impairment using easy-to-collect variables by non-invasive methods for community-dwelling elderly is useful prior to conducting such a comprehensive evaluation. This study aimed to develop a machine learning-based predictive model for future cognitive impairment. A total of 3424 community elderly without cognitive impairment were included from the nationwide dataset. The gradient boosting machine (GBM) was exploited to predict cognitive impairment after 2 years. The GBM performance was good (sensitivity = 0.967; specificity = 0.825; and AUC = 0.921). This study demonstrated that a machine learning-based predictive model might be used to screen future cognitive impairment using variables, which are commonly collected in community health care institutions. With efforts of enhancing the predictive performance, such a machine learning-based approach can further contribute to the improvement of the cognitive function in community elderly. More... »

PAGES

3335

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-39478-7

DOI

http://dx.doi.org/10.1038/s41598-019-39478-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112512791

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30833698


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