Random Forest and K-Means Clustering Algorithms to Classify of <sup>18</sup>F-Florbetapir Brain PET
Bibliographic record
Abstract
This paper explores and compares the use of two common machine learning (ML) algorithms, random forests (RF) and k-means clustering (KMC), for classifying18F-florbetapir brain PET as positive or negative for amyloid deposition. The pilot dataset consists of 6518F-Florbetapir PET and corresponding MRI studies taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI), in patients with mild cognitive impairment (MCI). Each PET scan was read as positive or negative for amyloid deposition by two physicians dual board certified in nuclear medicine and radiology with final interpretation based on consensus. This clinical interpretation of the PET scans served as the gold standard. Using an image processing pipeline, standardized uptake value ratios (SUVR) were computed in 57 brain regions, with normalization to the cerebellar gray matter. The RF algorithm had a slightly higher classification accuracy (91±6%) compared with the KMC algorithm (81±3%), using 4-fold cross-validation. However, the KMC algorithm had lower computational cost and may highlight equivocal cases on clinical interpretation. Further investigation is ongoing.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".