树集成方法和包裹识别与阿尔茨海默病相关的大脑区域

M. Wehenkel, C. Bastin, C. Phillips, P. Geurts
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引用次数: 8

摘要

几年来,机器学习方法在神经成像领域得到了越来越多的研究,以帮助诊断痴呆症。为此,本文提出了一种新的基于脑包裹、群体选择和树集成算法的模式识别技术。除了预测性能优于传统方法外,该方法还提供了与阿尔茨海默病预后有关的大脑区域的简单解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tree ensemble methods and parcelling to identify brain areas related to Alzheimer’s disease
For several years, machine learning approaches have been increasingly investigated in the neuroimaging field to help the diagnosis of dementia. To this end, this work proposes a new pattern recognition technique based on brain parcelling, group selection and tree ensemble algorithms. In addition to prediction performance competitive with more traditional approaches, the method provides easy interpretation about the brain regions involved in the prognosis of Alzheimer’s disease.
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