XGBoost for Interpretable Alzheimer’s Decision Support
Bibliographic record
Abstract
Despite their necessity in directing patient care worldwide, simple and accurate diagnostic tools for early Alzheimer’s disease (AD) do not exist. To support healthcare decision-making and planning, this research leverages large, multi-site accessible data and state-of-the-art supervised machine learning (XGBoost) to enable rapid, accurate, low-cost, accessible, non-invasive, interpretable, and early clinical evaluation of AD. Machine learning was employed to combine three key features: Everyday Cognition Questionnaire, Alzheimers Disease Assessment Scale, and Delayed Total Recall, achieving area under the receiver operating characteristic curves scores consistently above 97%. The selected features are important because they are non-invasive and easily collected. Low performance on delayed recall alone appears to distinguish most AD patients, consistent with the pathophysiology of AD where individuals having problems storing new information into long-term memory. Distinguishing this research from existing literature was the focus of enhancing the model's interpretability while maintaining performance of more complex and opaque models. The interpretable model enables understanding of the decision process, vital for clinical adoption of machine learning tools in AD evaluation. In summary, we present a methodology which identified accessible and noninvasive features, each with their absolute thresholds, together with a clinically operable decision route, to accurately and rapidly detect, differentiate, and diagnose Alzheimer's disease patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".