ArticlePloS one2021
Random forest model for feature-based Alzheimer's disease conversion prediction from early mild cognitive impairment subjects.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal.BMC geriatrics · 2024Pooled it
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Interpretable machine learning for dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Pooled it
- Comparative Analysis by Machine Learning of Geriatric Frailty and Alzheimer's Disease Classification Using Independent Datasets.Life (Basel, Switzerland) · 2026Article
- Machine learning and artificial intelligence in liquid biopsy-based early detection of pancreatic cancer: a scoping review.BJC reports · 2026Review
- Decoupling MCI-specific signatures from shared neurobiological substrates of cognitive aging via deep learning.NPJ digital medicine · 2026Article
- SHAP-based explainable machine learning analysis of reward-related neural connectivity to predict preadolescent irritability.Neuroimage. Reports · 2026Article
- Explainable hierarchical machine-learning approaches for multimodal prediction of conversion from mild cognitive impairment to Alzheimer's disease.Physical and engineering sciences in medicine · 2025Article
- Machine learning applications in forecasting patient satisfaction and clinical outcomes after carpal tunnel release: a retrospective study.BMC musculoskeletal disorders · 2025Article
- A comprehensive interpretable machine learning framework for mild cognitive impairment and Alzheimer's disease diagnosis.Scientific reports · 2025Article
- Exosome-Machine Learning Integration in Biomedicine: Advancing Diagnosis and Biomarker Discovery.Current medicinal chemistry · 2025Review
- Machine learning-based risk prediction model for cognitive dysfunction in elderly individuals.PloS one · 2025Article
- A comparative study of risk factors in predictive models for cognitive dysfunction in patients with leukoaraiosis based on machine learning algorithms.Frontiers in neuroscience · 2025Article
- HiMAL: Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting Alzheimer's disease progression.JAMIA open · 2024Article
- TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.Bioinformatics (Oxford, England) · 2024Article
- AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.Briefings in bioinformatics · 2024Article
- Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.Lipids in health and disease · 2024Article
- Gut-resident microorganisms and their genes are associated with cognition and neuroanatomy in children.Science advances · 2023Article
- Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden.Neuropsychologia · 2023Article
- Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals.Age and ageing · 2023Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Alzheimer's Disease (AD) conversion prediction from the mild cognitive impairment (MCI) stage has been a difficult challenge. This study focuses on providing an individualized MCI to AD conversion prediction using a balanced random forest model that leverages clinical data. In order to do this, 383 Early Mild Cognitive Impairment (EMCI) patients were gathered from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Of these patients, 49 would eventually convert to AD (EMCI_C), whereas the remaining 334 did not convert (EMCI_NC). All of these patients were split randomly into training and testing data sets with 95 patients reserved for testing. Nine clinical features were selected, comprised of a mix of demographic, brain volume, and cognitive testing variables. Oversampling was then performed in order to balance the initially imbalanced classes prior to training the model with 1000 estimators. Our results showed that a random forest model was effective (93.6% accuracy) at predicting the conversion of EMCI patients to AD based on these clinical features. Additionally, we focus on explainability by assessing the importance of each clinical feature. Our model could impact the clinical environment as a tool to predict the conversion to AD from a prodromal stage or to identify ideal candidates for clinical trials.
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