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Updated: Nov 4, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation.

Iman Beheshti, M A Ganaie, Vardhan Paliwal

IEEE Journal of Biomedical and Health Informatics

|May 24, 2021

Summary

This summary is machine-generated.

Different regression algorithms significantly impact brain age estimation accuracy. Quadratic Support Vector Regression yielded the highest accuracy, suggesting advanced machine learning improves clinical brain age predictions.

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Last Updated: Nov 4, 2025

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Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Machine learning (ML) algorithms are integral to brain age estimation frameworks.
A comprehensive evaluation of regression algorithms' impact on prediction accuracy is lacking.

Purpose of the Study:

To assess the efficiency of various regression algorithms in brain age estimation.
To compare prediction accuracy across 22 different regression algorithms.

Main Methods:

A brain age estimation framework was developed using 788 cognitively healthy individuals for training.
22 regression algorithms were applied and evaluated on independent test sets (cognitively healthy, mild cognitive impairment, and Alzheimer's disease patients).

Main Results:

Prediction accuracy varied, with Mean Absolute Error (MAE) ranging from 4.63 to 7.14 years and R-squared (R²) from 0.76 to 0.88.
Quadratic Support Vector Regression achieved the highest accuracy (MAE = 4.63 yrs, R² = 0.88).
Binary Decision Tree algorithm showed the lowest accuracy (MAE = 7.14 yrs, R² = 0.76).

Conclusions:

Regression algorithms significantly influence the accuracy of brain age prediction frameworks.
Utilizing advanced machine learning algorithms can enhance the precision of brain age predictions in clinical applications.