Optimizing Feature Panels for Effective Model Training Toward Early Diagnosis of Alzheimer's Disease
Bibliographic record
Abstract
In this work, we aim to design effective feature se-lection strategies to identify relevant features to expedite machine learning model training for diagnosing Alzheimer's disease. In the first method, we introduce ranks features based on their dependency on the diagnosis by employing metrics such as Mutual Information, Symmetric Uncertainty, and Cramer's V panels via iteratively selecting top features and gradually increasing the panel size. We then propose a second method that determines the relevant features by estimating the Euclidean distance between samples and class means for each feature, employing a thresh-old to filter out irrelevant features. Candidate panels identified using each method are extensively tested on two datasets. Even though panels formed using the first dataset fail to meet the minimum performance criteria of 75% sensitivity and specificity, those formed using the second set achieve a significantly high accuracy up to 99.53%, 100% sensitivity, and 95% specificity. The results demonstrate the viability of our two methods, potentially paving the way for low-cost, non-invasive early detection tools for Alzheimer's disease.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".