Sarica Alessia, Cerasa Antonio, Quattrone Aldo
Institute of Bioimaging and Molecular Physiology, National Research Council, Catanzaro, Italy.
Institute of Neurology, University Magna Graecia, Catanzaro, Italy.
Front Aging Neurosci. 2017 Oct 6;9:329. doi: 10.3389/fnagi.2017.00329. eCollection 2017.
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. 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: . The query was then searched in four well-known scientific databases: Pubmed, Scopus, Google Scholar and Web of Science. 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. 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.
机器学习分类是过去几年中最重要的计算发展，以满足临床医生对自动早期诊断和预后的首要需求。如今，随机森林（RF）算法已成功应用于许多科学领域，用于减少高维和多源数据。我们的目的是探索RF在单模态和多模态神经影像数据上应用于阿尔茨海默病预测的技术现状。我们按照PRISMA指南对该研究领域进行了系统综述。具体而言，我们使用布尔运算符构建了一个高级查询，如下所示： 。然后在四个著名的科学数据库中进行搜索：PubMed、Scopus、谷歌学术和科学网。经过定量和定性筛选，12篇发表于2007年至2017年之间的文章被纳入本系统综述。从这些研究中得到的经验表明，当RF应用于多模态数据以预测从轻度认知障碍（MCI）到阿尔茨海默病（AD）的转化时，它产生了迄今为止最好的准确率之一。此外，RF在抗过拟合鲁棒性、处理高度非线性数据的能力、存在异常值时的稳定性以及主要在应用于多模态神经影像数据（如MRI形态计量学、扩散张量成像和PET图像）时进行高效并行处理的机会方面具有重要优势。我们讨论了RF的优势，同时也考虑了可能的局限性，并鼓励进一步研究该算法与其他常用分类方法的比较，特别是在从MCI进展到AD的早期预测方面。
