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      Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review

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          Objective: Machine learning classification has been the most important computational development in the last years to satisfy the primary need of clinicians for automatic early diagnosis and prognosis. Nowadays, Random Forest (RF) algorithm has been successfully applied for reducing high dimensional and multi-source data in many scientific realms. Our aim was to explore the state of the art of the application of RF on single and multi-modal neuroimaging data for the prediction of Alzheimer's disease.

          Methods: A systematic review following PRISMA guidelines was conducted on this field of study. In particular, we constructed an advanced query using boolean operators as follows: (“random forest” OR “random forests”) AND neuroimaging AND (“alzheimer's disease” OR alzheimer's OR alzheimer) AND (prediction OR classification) . The query was then searched in four well-known scientific databases: Pubmed, Scopus, Google Scholar and Web of Science.

          Results: Twelve articles—published between the 2007 and 2017—have been included in this systematic review after a quantitative and qualitative selection. The lesson learnt from these works suggest that when RF was applied on multi-modal data for prediction of Alzheimer's disease (AD) conversion from the Mild Cognitive Impairment (MCI), it produces one of the best accuracies to date. Moreover, the RF has important advantages in terms of robustness to overfitting, ability to handle highly non-linear data, stability in the presence of outliers and opportunity for efficient parallel processing mainly when applied on multi-modality neuroimaging data, such as, MRI morphometric, diffusion tensor imaging, and PET images.

          Conclusions: We discussed the strengths of RF, considering also possible limitations and by encouraging further studies on the comparisons of this algorithm with other commonly used classification approaches, particularly in the early prediction of the progression from MCI to AD.

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          Most cited references 41

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

          David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses
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            The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration

            Systematic reviews and meta-analyses are essential to summarise evidence relating to efficacy and safety of healthcare interventions accurately and reliably. The clarity and transparency of these reports, however, are not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (quality of reporting of meta-analysis) statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realising these issues, an international group that included experienced authors and methodologists developed PRISMA (preferred reporting items for systematic reviews and meta-analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this explanation and elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA statement, this document, and the associated website ( should be helpful resources to improve reporting of systematic reviews and meta-analyses.
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              Bagging predictors

               Leo Breiman (1996)

                Author and article information

                Front Aging Neurosci
                Front Aging Neurosci
                Front. Aging Neurosci.
                Frontiers in Aging Neuroscience
                Frontiers Media S.A.
                06 October 2017
                : 9
                1Institute of Bioimaging and Molecular Physiology, National Research Council , Catanzaro, Italy
                2Institute of Neurology, University Magna Graecia , Catanzaro, Italy
                Author notes

                Edited by: Juan Manuel Gorriz, University of Granada, Spain

                Reviewed by: Stavros I. Dimitriadis, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, United Kingdom; Feng Liu, Tianjin Medical University General Hospital, China

                *Correspondence: Alessia Sarica sarica@
                Copyright © 2017 Sarica, Cerasa and Quattrone.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 3, Tables: 1, Equations: 1, References: 41, Pages: 12, Words: 9103


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