Wrappers Feature Selection in Alzheimer's Biomarkers Using kNN and SMOTE Oversampling
Bibliographic record
Abstract
Biomarkers are a characteristic that is objectively measured and eval-uated as an indicator of normal biological processes, pathogenic processes or phar-macological responses to a therapeutic intervention. The combination of dierentbiomarker modalities often allows an accurate diagnosis classication. In Alzheimer'sdisease (AD), biomarkers are indispensable to identify cognitively normal individ-uals destined to develop dementia symptoms. However, using the combination ofcanonical AD biomarkers, studies have repeatedly shown poor classication ratesto dierentiate between AD, mild cognitive impairment and control individuals.Furthermore, the design of classiers to access multiple biomarker combinationsincludes issues such as imbalance classes and missing data. Since the numberbiomarker combinations is large then wrappers are used to avoid multiple com-parisons. Here, we compare the ability of three wrappers feature selection methodsto obtain biomarker combinations which maximize classication rates. Also, ascriterion to the wrappers feature selection we use the k-nearest neighbor classi-er with balance aids, random undersampling and SMOTE. Overall, our analysesshowed how biomarkers combinations aects the classier accuracy and how imbal-ance strategy improve it. We show that non-dening and non-cognitive biomarkershave less accuracy than cognitive measures when classifying AD. Our approach sur-pass in average the support vector machine and the weighted k-nearest neighborsclassiers and reaches 94.34 ± 3.91% of accuracy reproducing class denitions.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".