ArticleScientific reports2019
Prediction of future cognitive impairment among the community elderly: A machine-learning based approach.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Functional disability screening in the elderly: a machine learning approach with ELSI-Brazil data.GeroScience · 2026Article
- Construction of a classification model for dementia among Brazilian adults aged 50 and over.Frontiers in aging neuroscience · 2026Article
- A cognitive multiplex network approach to investigate mental navigation and predict high-level cognition.Behavior research methods · 2025Article
- Identifying dementia neuropathology using low-burden clinical data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
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- The cognitive consequences of fear of falling: assessing the mediating role of social engagement and physical activity.Innovation in aging · 2025Article
- Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification.Frontiers in radiology · 2025Article
- Machine learning-based risk prediction model for cognitive dysfunction in elderly individuals.PloS one · 2025Article
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- Development and interpretation of a machine learning predictive model for early cognitive impairment in hypertension associated with environmental factors.Frontiers in cardiovascular medicine · 2025Article
- Frequency, sociodemographic, and neuropsychological features of patients with subjective cognitive decline diagnosed using different neuropsychological criteria.Alzheimer's research & therapy · 2024Article
- The Potential of Automated Assessment of Cognitive Function Using Non-Neuroimaging Data: A Systematic Review.Journal of clinical medicine · 2024Review
- Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).European journal of medical research · 2024Article
- Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study.The Lancet regional health. Western Pacific · 2024Article
- Visual and auditory attention defects in children with intermittent exotropia.Italian journal of pediatrics · 2024Article
- Importance of serum albumin in machine learning-based prediction of cognitive function in the elderly using a basic blood test.Frontiers in neurology · 2024Article
- Developing an intelligent prediction system for successful aging based on artificial neural networks.International journal of preventive medicine · 2024Article
- Article
- Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study.BMC geriatrics · 2023Article
- Differentiating Alzheimer's disease from mild cognitive impairment: a quick screening tool based on machine learning.BMJ open · 2023Article
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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.
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