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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Machine Learning-Based Age Prediction with Feature Subset Selection from Magnetic Resonance Angiography Data.

Hoon-Seok Yoon¹, Yoon-Chul Kim¹

¹Medical Artificial Intelligence Laboratory, Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, Korea.

Healthcare Informatics Research

|August 21, 2025

Summary

Machine learning models can predict vascular age using brain artery measurements. Feature selection improved accuracy, highlighting tortuosity as a key indicator of vascular aging.

Keywords:

Arteries Circle of Willis Computer-Assisted Image Processing Machine Learning Magnetic Resonance Imaging

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Area of Science:

Neuroimaging
Biomedical Engineering
Artificial Intelligence

Background:

Cerebrovascular health assessment is crucial for aging populations.
Magnetic Resonance Angiography (MRA) provides detailed intracranial arterial data.
Machine learning (ML) offers potential for analyzing complex vascular features.

Purpose of the Study:

To assess ML model effectiveness in predicting vascular age using MRA data.
To identify critical vascular features for age prediction.
To evaluate the impact of feature selection on prediction accuracy.

Main Methods:

Analysis of 3D time-of-flight MRA data from 171 subjects.
Extraction of 169 features including tortuosity and diameter metrics.
Training and validation of five ML models with correlation-based feature selection (CFS) and Relief-F.

Main Results:

The random forest model with a CFS-selected feature subset achieved the best performance (RMSE: 14.0 years, R2: 0.275).
Tortuosity metrics were identified as more significant predictors than diameter statistics.
Feature selection enhanced ML model performance compared to using all extracted features.

Conclusions:

ML-based age prediction using intracranial arterial features is feasible.
Feature selection methods like CFS improve prediction accuracy.
Vascular tortuosity is a key factor in predicting vascular age.