Observational studyScientific reports2020
Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods.
Observational study in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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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
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association of nonpharmacological interventions for cognitive function in older adults with mild cognitive impairment: a systematic review and network meta-analysis.Aging clinical and experimental research · 2023Pooled it
- Prediction of cognitive test scores: a comparison of brain structure, health, demographic, and cognitive data across adulthood.GeroScience · 2026Article
- Comparative assessment of machine learning models for polymer solution viscosity prediction in enhanced oil recovery.Scientific reports · 2026Article
- Integrating eye-tracking into psychometric research: computational approaches to explore response burden in questionnaire completion.Frontiers in psychology · 2026Article
- XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults.Scientific reports · 2025Article
- Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence.Brain informatics · 2025Article
- Rationale and protocol of the LEAD 2.0 study: a randomized controlled trial assessing the feasibility of a virtually delivered 6-month exercise and nutrition intervention in older adults with subjective cognitive decline (SCD).Pilot and feasibility studies · 2025Article
- Experimental validation of machine learning for contamination classification of polluted high voltage insulators using leakage current.Scientific reports · 2025Article
- Classifying metro drivers' cognitive distractions during manual operations using machine learning and random forest-recursive feature elimination.Scientific reports · 2025Article
- An improved spatially downscaled solar-induced chlorophyll fluorescence dataset from the TROPOMI product.Scientific data · 2025Article
- Deficits in reaching movements under visual interference as a novel diagnostic marker for mild cognitive impairment.Scientific reports · 2025Article
- Article
- Odor classification: Exploring feature performance and imbalanced data learning techniques.PloS one · 2025Article
- Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.BMC medical informatics and decision making · 2024Article
- Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.Diagnostics (Basel, Switzerland) · 2024Article
- Neurovascular coupling, functional connectivity, and cerebrovascular endothelial extracellular vesicles as biomarkers of mild cognitive impairment.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Association between Subjective Cognitive Complaints and Sleep Disturbance among Community-Dwelling Elderly Individuals in Japan.Healthcare (Basel, Switzerland) · 2024Article
- Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer.Journal for immunotherapy of cancer · 2024Article
- Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach.Frontiers in aging neuroscience · 2024Article
- Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.
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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.