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• W2918370085 abstract "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." @default.
• W2918370085 created "2019-03-11" @default.
• W2918370085 creator A5004713384 @default.
• W2918370085 date "2019-03-04" @default.
• W2918370085 modified "2023-10-01" @default.
• W2918370085 title "Prediction of future cognitive impairment among the community elderly: A machine-learning based approach" @default.
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• W2918370085 doi "https://doi.org/10.1038/s41598-019-39478-7" @default.
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