ArticleScientific programming2020
Machine Learning for the Preliminary Diagnosis of Dementia.
Article in Scientific programming, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The trial behind it
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
26 citing papers in PubMed.
- Classification of patients with Alzheimer's disease using the arterial pulse spectrum and a multilayer-perceptron analysis.Scientific reports · 2021Trial
- Article
- Use of Machine Learning to Identify Markers of Risk for Fragile X-Associated Tremor/Ataxia Syndrome: A Preliminary Analysis.Annals of neurology · 2026Article
- Can machine learning assist us in the classification of older patients suffering from dementia based on classic neuropsychological tests and a new financial capacity test performance?Journal of neuropsychology · 2025Article
- Development of machine learning models for prediction of current and future dementia.PloS one · 2025Article
- Quantum AI for psychiatric diagnosis: enhancing dementia classification with quantum machine learning.Frontiers in psychiatry · 2025Article
- Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer's disease spectrum: demonstration analyses in the COMPASS-ND study.Frontiers in aging neuroscience · 2025Article
- AlzheimerViT: harnessing lightweight vision transformer architecture for proactive Alzheimer's screening.Frontiers in medicine · 2025Article
- Music-induced physiological markers for detecting Alzheimer's disease using machine learning.Frontiers in aging neuroscience · 2025Article
- Machine learning models identify predictive features of patient mortality across dementia types.Communications medicine · 2024Article
- Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-effective Model Selection Algorithm.IEEE access : practical innovations, open solutions · 2024Article
- Immediate word recall in cognitive assessment can predict dementia using machine learning techniques.Alzheimer's research & therapy · 2023Article
- Measurement of Prevalent Versus Incident Dementia Cases in Epidemiologic Studies.American journal of epidemiology · 2023Article
- Predictive Models and Features of Patient Mortality across Dementia Types.Research square · 2023Article
- Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- The application of cross-sectionally derived dementia algorithms to longitudinal data in risk factor analyses.Annals of epidemiology · 2023Article
- Applications of artificial intelligence in dementia research.Cambridge prisms. Precision medicine · 2023Review
- Examining Cognitive Factors for Alzheimer's Disease Progression Using Computational Intelligence.Healthcare (Basel, Switzerland) · 2022Article
- Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification.Journal of personalized medicine · 2022Article
- Screening dementia and predicting high dementia risk groups using machine learning.World journal of psychiatry · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Objective: The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods: We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results: Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and Conclusion: The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.
Identifiers
What OpenQuestion holds
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.