Comparison of the WEKA and SVM-light based on support vector machine in classifying Alzheimer’s disease using structural features from brain MR imaging
Bibliographic record
Abstract
Abstract The aim was to compare the WEKA and SVM-light software based on support vector machine (SVM) algorithm using features from brain T1-weighted MRI for differentiating AD patients and normal elderly subjects. The FreeSurfer software was used to extract cerebral volumes and thicknesses from T1-weighted brain MRI (100 AD patients and 100 normal elderly subjects). Seven structures were selected based on literature reviews consisting of hippocampus and amygdala volume, entorhinal cortex thickness of both hemispheres, and total gray matter volume. Relative volume of hippocampus, amygdala, and total gray matter were normalized by total intracranial volume (TIV). Fifteen combinations of seven structures were applied as input features to WEKA and SVM-light. The receiver operating characteristic (ROC) analysis and area under the curve (AUC) were used to evaluate the classification performance. The combination of hippocampus relative volume and entorhinal cortex thickness provided the highest classification performance and the AUC values were 0.913 and 0.918 for WEKA and SVM-light, respectively. There was no statistically difference of the AUC values (p-value > 0.05) between two software using the same input features. In conclusion, there was no statistically difference between the use of WEKA and SVM-light software for differentiating AD patients and normal elderly subjects.
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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".