ArticleBMJ open2025
Advancing Alzheimer's disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Interpretable machine learning model based on multimodal MRI radiomics for Alzheimer's disease diagnosis.Frontiers in aging neuroscience · 2026Article
- Precision neurodiversity: personalized brain network architecture as a window into cognitive variability.Frontiers in human neuroscience · 2025Review
- Machine learning for prediction ofFrontiers in medicine · 2025Article
- Long-term Alzheimer's disease mortality prediction in adults aged ≥60 years: A prospective cohort study benchmarking survival machine learning algorithms.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAlzheimer's disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited accessibility restrict practical application. This study aimed to develop a convenience, efficient prediction model for AD risk using machine learning techniques. DESIGN AND
settingWe conducted a cross-sectional study with participants aged 60 and older from the National Alzheimer's Coordinating Center. We selected personal characteristics, clinical data and psychosocial factors as baseline predictors for AD (March 2015 to December 2021). The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. An oversampling method was applied to balance the data set.
interventionsThis study has no interventions.
participantsThe study included 2379 participants, of whom 507 were diagnosed with AD. PRIMARY AND SECONDARY OUTCOME MEASURES: Including accuracy, precision, recall, F1 score, etc.
results11 variables were critical in the training phase, including educational level, depression, insomnia, age, Body Mass Index (BMI), medication count, gender, stenting, systolic blood pressure (sbp), neurosis and rapid eye movement. The XGBoost model exhibited superior performance compared with other models, achieving area under the curve of 0.915, sensitivity of 76.2% and specificity of 92.9%. The most influential predictors were educational level, total medication count, age, sbp and BMI.
conclusionsThe proposed classifier can help guide preclinical screening of AD in the elderly population.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.