ArticleJournal of Alzheimer's disease : JAD2023
A Machine Learning Approach for Early Diagnosis of Cognitive Impairment Using Population-Based Data.
Article in Journal of Alzheimer's disease : JAD, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- The Brain Vulnerability Index: Development and Validation of a Machine Learning-Derived, Community-Informed Geospatial Risk Score for Cognitive Impairment.Research square · 2026Article
- Early cognitive screening for individuals on the dementia continuum: A novel approach amid current trends.Journal of Alzheimer's disease : JAD · 2026Review
- Development of a clinical nomogram to assess the risk of cognitive impairment in community-dwelling middle-aged and older adults.BMC neurology · 2026Article
- Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU.Intensive care medicine experimental · 2026Article
- Predicting Cognitive Outcome Through Nutrition and Health Markers Using Supervised Machine Learning.The Journal of nutrition · 2025Article
- Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study.BMC public health · 2025Article
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- A point-based cognitive impairment scoring system for southeast Asian adults.The journal of prevention of Alzheimer's disease · 2025Article
- Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.Alzheimer's research & therapy · 2025Observational
- Development and interpretation of a machine learning predictive model for early cognitive impairment in hypertension associated with environmental factors.Frontiers in cardiovascular medicine · 2025Article
- Machine learning-based early screening of mild cognitive impairment using nutrition-related biomarkers and functional indicators.Frontiers in aging neuroscience · 2025Article
- Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.JMIR formative research · 2024Article
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- Explainable machine learning models for early gastric cancer diagnosis.Scientific reports · 2024Article
- Predicting early Alzheimer's with blood biomarkers and clinical features.Scientific reports · 2024Article
- A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study.Frontiers in public health · 2023Article
- Red-flagging multimorbidity clusters for Alzheimer's disease risk using explainable machine learning: Evidence from a national emergency department sample.Journal of Alzheimer's disease reportsArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe major mechanisms of dementia and cognitive impairment are vascular and neurodegenerative processes. Early diagnosis of cognitive impairment can facilitate timely interventions to mitigate progression.
objectiveThis study aims to develop a reliable machine learning (ML) model using socio-demographics, vascular risk factors, and structural neuroimaging markers for early diagnosis of cognitive impairment in a multi-ethnic Asian population.
methodsThe study consisted of 911 participants from the Epidemiology of Dementia in Singapore study (aged 60- 88 years, 49.6% male). Three ML classifiers, logistic regression, support vector machine, and gradient boosting machine, were developed. Prediction results of independent classifiers were combined in a final ensemble model. Model performances were evaluated on test data using F1 score and area under the receiver operating curve (AUC) methods. Post modelling, SHapely Additive exPlanation (SHAP) was applied on the prediction results to identify the predictors that contribute most to the cognitive impairment prediction.
findingsThe final ensemble model achieved a F1 score and AUC of 0.87 and 0.80 respectively. Accuracy (0.83), sensitivity (0.86), specificity (0.74) and predictive values (positive 0.88 negative 0.72) of the ensemble model were higher compared to the independent classifiers. Age, ethnicity, highest education attainment and neuroimaging markers were identified as important predictors of cognitive impairment.
conclusionThis study demonstrates the feasibility of using ML tools to integrate multiple domains of data for reliable diagnosis of early cognitive impairment. The ML model uses easy-to-obtain variables and is scalable for screening individuals with a high risk of developing dementia in a population-based setting.
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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 OpenQuestion graph.