Alzheimer’s Disease Stage Classification using Blood Data
Bibliographic record
Abstract
In this work, we aim to evaluate the performance of Machine Learning models in the classification of Alzheimer’s patients into disease stages using two feature selection methods proposed in our previous work. The first method identifies relevant features by ranking them according to their dependency on the diagnosis, which is measured using metrics such as Mutual Information, Symmetric Uncertainty and Cramer’s V. The second method filters relevant features by applying a threshold to the Euclidean distance between the class means for each feature. Three new approaches are proposed to accommodate multiple classes in the Euclidean distance-based feature selection method. These approaches involve scoring each feature using the minimum, maximum and average of the Euclidean distances between the class means. Using two datasets, we extensively tested all possible combinations of relevant features identified using the two methods, ensuring minimal correlation among features in each panel. The panels achieved accuracies as high as 75.17% and 85.46% on the first and second datasets, respectively. The performances achieved in this work demonstrate the feasibility of using blood-based biomarkers in Alzheimer’s disease stage classification and could help in developing accurate low-cost and non-invasive early detection tools that could aid in Alzheimer’s disease diagnosis.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".