A Preliminary Feasibility Study on Screening Cognitive Impairment based on Multi‐Modal Biomarkers and Stacking Ensemble AI Approach
Bibliographic record
Abstract
Abstract Background Early screening of cognitive impairment is crucial for patients demanding timely treatment. The need for cost‐effective, easily accessible, and accurate tools to detect cognitive decline rapidly progressed with the breakout of the COVID‐19 pandemic. In this work, we proposed a mobile‐app based cognitive assessment tool, “Alzguard‐D”, equipped with two digital biomarkers along with a cognitive task. Next, we investigated the efficacy of “Alzguard‐D” in early screening of cognitive impairment using a machine learning approach. Method Nine “Alzguard‐D” tasks were designed based on three biomarkers: Keystroke, speech and eye movement (Table 1; Figure 1). Total of 289 participants were recruited from the national institute of dementia, welfare center, and nursing homes. The participants first completed the Korean Mini‐Mental State Examination (K‐MMSE; 2nd Edition), then performed all tasks in “Alzguard‐D”. “Alzguard‐D” took approximately 20 to 30 minutes. The participants were then categorized to healthy control (HC; n = 241) and cognitively impaired (CI; n = 48), defined as when the scores of K‐MMSE were one standard deviation below the age, education and gender‐matched norm. Given the data, we investigated the extent in which the combination of digital biomarkers and cognitive tasks improves the screening accuracy. We used a stacking ensemble model where the first stack analyzed speech and eye movement, and the second performed the classification. Result The screening result confirmed that the addition of digital biomarkers collected by “Alzguard‐D” significantly improved the classification performance (p < 0.0001; sample paired t‐test). CatBoost algorithm statistically outperformed all other candidate algorithms (Bagging, Logistic Regression, LGBM, Naïve Bayes, XGBoost, Random Forest, SVM, Gradient Boosting). We then conducted an ablation study with three classifiers trained with (i) Cog+Bio, (ii) Bio and (iii) Cog. Cog+Bio achieved the highest AUC score, 0.876 (max: 0.942), followed by Bio with 0.783 (max: 0.845), then Cog with 0.677 (max: 0.726) (Figure 2). Conclusion A combination of cognitive and digital biomarkers significantly increased the screening performance of cognitive impairment, and employing an intermediate feature‐level ensemble approach to effectively analyze the collected multi‐modal data increased the AUC levels.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".