Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation

Brain age prediction using machine learning has become a promising approach in neuroimaging and neuroscience research.By estimating the biological age of the brain from MRI scans and comparing it with chronological age, researchers can identify signs of accelerated aging, which is often linked to neurodegenerative diseases and cognitive decline.This paper provides a comprehensive evaluation of machine learning algorithms applied to brain age prediction.We explore a range of traditional and deep learning models, compare their accuracy, generalizability, and interpretability, and assess their potential in clinical applications.The study emphasizes the value of brain age as a biomarker and outlines the challenges that remain, including dataset variability, model bias, and explainability.