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Neural Regen Res. 2018 Jun;13(6):962-970. doi: 10.4103/1673-5374.233433.

How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database.

Author information

1
Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University; Cardiff University Brain Research Imaging Centre, School of Psychology; School of Psychology; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology; Neuroscience and Mental Health Research Institute; MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff, UK.
2
High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Stuttgart, Germany; Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Abstract

Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines (behavioural neuroscience, genetics, cognitive psychology, etc.) working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest (RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer's disease (AD), the conversion from mild cognitive impairment (MCI) and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1st position in an international challenge for automated prediction of MCI from MRI data.

KEYWORDS:

Alzheimer's disease; biomarker; classification; machine learning; magnetic resonance imaging; mild cognitive impairment; neuroimaging; random forest

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