Screening mild cognitive impairment using aspects of personal, social, and functional lifestyle: Machine Learning Approaches
Bibliographic record
Abstract
OBJECTIVE: Mild cognitive impairment (MCI) signals cognitive decline beyond normal aging and increases dementia risk. Early identification enables preventative interventions, yet many patients in primary care go undetected. This study examines whether machine learning (ML) models can predict MCI using routinely collected personal, social, and functional lifestyle factors and identifies the most important predictors. METHODS: Data from round 2 and 3 of the National Social Life, Health, and Aging Project was used, including 4,586 older adults with complete Montreal Cognitive Assessment (MoCA) scores. Predictors included demographics, childhood experiences, health behaviors, psychosocial measures, and functional difficulties. Eight ML models-including elastic net, multivariate adaptive regression splines, random forest, oblique random forest, boosted trees, decision trees, and a stacked ensemble-were trained and tuned using repeated cross-validation, with 20% of the dataset withheld for final testing. Model performance was assessed using area under the receiver operator curve (AUROC), accuracy, sensitivity, specificity, and Matthew's correlation coefficient (MCC). RESULTS: Most models achieved good discrimination (AUROC > 0.8), with the stacked ensemble performing best (AUROC = 0.823; MCC = 0.462). The best individual model was logistic regression (AUROC = 0.818). Across models, key predictors of MCI included age, ethnicity, functional difficulties, social disconnectedness, and perceived stress. DISCUSSION: Logistic regression outperformed more complex machine learning models, providing the best combination of predictive accuracy and interpretability for identifying MCI. Across models, age, ethnicity, functional difficulties, social disconnectedness, and stress consistently emerged as key predictors, highlighting their central role in cognitive health. These findings suggest that psychosocial and functional measures can serve as practical indicators for those who need to be screened early for MCI, offering an opportunity for timely intervention and support. However, future work should include longitudinal data and clinical diagnoses to validate and refine these predictive tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".