多视图技术在NHANES数据集上的应用
Application of multiview techniques to NHANES dataset
摘要：
使用健康数据集的疾病预测或分类涉及使用与疾病相关联的众所周知的预测因子作为模型的特征。这项研究考虑了个人健康的多个数据组成部分，使用变量之间的关系来生成可能提高疾病分类模型性能的特征。为了从数据的不同方面捕获信息，该项目使用多视图学习方法，使用典型相关分析（CCA），这是一种找到两个数据视图之间具有最大相关性的投影的技术。从NHANES调查（1999-2014）收集的数据类别被用作视图来学习多视图表示。通过将它们作为糖尿病分类任务中的特征来证明表示的有用性。
英文摘要：
Disease prediction or classification using health datasets involve using well-known predictors associated with the disease as features for the models. This study considers multiple data components of an individual's health, using the relationship between variables to generate features that may improve the performance of disease classification models. In order to capture information from different aspects of the data, this project uses a multiview learning approach, using Canonical Correlation Analysis (CCA), a technique that finds projections with maximum correlations between two data views. Data categories collected from the NHANES survey (1999-2014) are used as views to learn the multiview representations. The usefulness of the representations is demonstrated by applying them as features in a Diabetes classification task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
