Artificial intelligence-based prediction of cognitive frailty: a clinical data approach
Bibliographic record
Abstract
Cognitive frailty (CF), defined as the co-existence of physical frailty and cognitive impairment without dementia, has two subtypes: reversible cognitive frailty (RCF) and potentially reversible cognitive frailty (PRCF). This study aimed to develop machine learning models for the early prediction of CF by incorporating both RCF and PRCF into a unified framework. Data from 2,173 participants were sourced from the AGELESS and MELoR cohort studies, including clinical, blood, urine, and health status information. Participants were grouped into six CF categories based on the FRAIL scale and MoCA scores from 2020 and 2022. Seven machine learning models, SVM, LR, KNN, RF, CART, LDA, and GNB, were trained using clinical, blood, and urine datasets. Clinical variables outperformed other data types, with SVM, LR, and CART models achieving 95% accuracy and AUC scores of 1.0, while blood- and urine-based models showed lower performance (AUCs of 0.8 and 0.89, respectively). F1 scores and ROC analysis confirmed the robustness of the clinical models, and k-fold and grid search cross-validations showed consistent performance on unseen data, indicating no overfitting. These findings highlight the superior predictive power of clinical variables for identifying both subtypes of CF, supporting their potential for use in early, accurate diagnosis. Nonetheless, external validation using independent cohorts is recommended to ensure broader applicability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".