Machine Learning-Based Age Prediction with Feature Subset Selection from Magnetic Resonance Angiography Data
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to evaluate the effectiveness of machine learning (ML) models using selected subsets of features to predict age based on intracranial arterial segments' tortuosity and diameter characteristics derived from magnetic resonance angiography (MRA) data. Additionally, this study aimed to identify key vascular features important for predicting vascular age. METHODS: Three-dimensional time-of-flight MRA image data from 171 subjects were analyzed. After annotating the endpoints for each arterial segment, 169 features-comprising tortuosity metrics and arterial segment diameter statistics-were extracted. Five ML models (random forest, linear regression, AdaBoost, XGBoost, and lightGBM) were trained and validated. Two feature selection methods, correlation-based feature selection (CFS) and Relief-F, were applied to identify optimal feature subsets. RESULTS: The random forest model utilizing the CFS-based 50% feature subset achieved the best performance, with a root mean square error of 14.0 years, a coefficient of determination (R2) of 0.275, and a Pearson correlation coefficient of 0.560. Tortuosity metrics (e.g., triangular index of the left posterior cerebral artery P1 segment) appeared more frequently than diameter statistics among the top five most important features. CONCLUSIONS: CFS-based feature selection enhanced the performance of ML-based age prediction compared with using the complete feature set. Linear regression consistently demonstrated the poorest performance across all evaluation metrics. ML-based age prediction using segmental tortuosity metrics and diameter statistics is feasible, potentially revealing significant features related to vascular aging.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it