Table 1_Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer’s disease spectrum: demonstration analyses in the COMPASS-ND study.docx
Bibliographic record
Abstract
Background Research in aging, impairment, and Alzheimer’s disease (AD) often requires powerful computational models for discriminating between clinical cohorts and identifying early biomarkers and key risk or protective factors. Machine Learning (ML) approaches represent a diverse set of data-driven tools for performing such tasks in big or complex datasets. We present systematic demonstration analyses to compare seven frequently used ML classifier models and two eXplainable Artificial Intelligence (XAI) techniques on multiple performance metrics for a common neurodegenerative disease dataset. The aim is to identify and characterize the best performing ML and XAI algorithms for the present data. Method We accessed a Canadian Consortium on Neurodegeneration in Aging dataset featuring four well-characterized cohorts: Cognitively Unimpaired (CU), Subjective Cognitive Impairment (SCI), Mild Cognitive Impairment (MCI), and AD (N = 255). All participants contributed 102 multi-modal biomarkers and risk factors. Seven ML algorithms were compared along six performance metrics in discriminating between cohorts. Two XAI algorithms were compared using five performance and five similarity metrics. Results Although all ML models performed relatively well in the extreme-cohort comparison (CU/AD), the Super Learner (SL), Random Forest (RF) and Gradient-Boosted trees (GB) algorithms excelled in the challenging near-cohort comparisons (CU/SCI). For the XAI interpretation comparison, SHapley Additive exPlanations (SHAP) generally outperformed Local Interpretable Model agnostic Explanation (LIME) in key performance properties. Conclusion The ML results indicate that two tree-based methods (RF and GB) are reliable and effective as initial models for classification tasks involving discrete clinical aging and neurodegeneration data. In the XAI phase, SHAP performed better than LIME due to lower computational time (when applied to RF and GB) and incorporation of feature interactions, leading to more reliable results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".