Statistical variability in comparing accuracy of neuroimaging based classification models via cross validation

Statistical variability in comparing accuracy of neuroimaging based classification models via cross validation

Abstract Machine learning (ML) has significantly transformed biomedical research, leading to a growing interest in model development to advance classification accuracy in various clinical applications. However, this progress raises essential questions regarding how to rigorously compare the accuracy...

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Bibliographic Details
Main Authors: Bahram Jafrasteh, Ehsan Adeli, Kilian M. Pohl, Amy Kuceyeski, Mert R. Sabuncu, Qingyu Zhao
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Machine learning
Cross validation
Reproducibility crisis
Statistical hypothesis testing
Online Access:https://doi.org/10.1038/s41598-025-12026-2
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https://doi.org/10.1038/s41598-025-12026-2

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