ArticleFrontiers in neuroscience2018
Random Forests Based Group Importance Scores and Their Statistical Interpretation: Application for Alzheimer's Disease.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Superficial and deep white matter brain alterations discriminate tinnitus in older adults.Brain structure & function · 2026Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification.Interdisciplinary sciences, computational life sciences · 2024Article
- Increased n-6 Polyunsaturated Fatty Acids Indicate Pro- and Anti-Inflammatory Lipid Modifications in Synovial Membranes with Rheumatoid Arthritis.Inflammation · 2023Article
- mSRFR: a machine learning model using microalgal signature features for ncRNA classification.BioData mining · 2022Article
- Identifying undercompensated groups defined by multiple attributes in risk adjustment.BMJ health & care informatics · 2021Article
- Characterization of a temporoparietal junction subtype of Alzheimer's disease.Human brain mapping · 2019Article
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Authors and funding
5 authors.
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Abstract
Machine learning approaches have been increasingly used in the neuroimaging field for the design of computer-aided diagnosis systems. In this paper, we focus on the ability of these methods to provide interpretable information about the brain regions that are the most informative about the disease or condition of interest. In particular, we investigate the benefit of group-based, instead of voxel-based, analyses in the context of Random Forests. Assuming a prior division of the voxels into non overlapping groups (defined by an atlas), we propose several procedures to derive group importances from individual voxel importances derived from Random Forests models. We then adapt several permutation schemes to turn group importance scores into more interpretable statistical scores that allow to determine the truly relevant groups in the importance rankings. The good behaviour of these methods is first assessed on artificial datasets. Then, they are applied on our own dataset of FDG-PET scans to identify the brain regions involved in the prognosis of Alzheimer's disease.
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