莱姆病患者调查数据的特征选择
Feature Selection on Lyme Disease Patient Survey Data
摘要：
莱姆病是一种快速增长的疾病，在医学界仍然知之甚少。关于患者何时以及为什么对治疗有反应或生病，什么样的治疗方法是有效的，甚至如何正确诊断疾病的关键问题在很大程度上仍然没有答案。我们通过将机器学习技术应用于非营利组织LymeDisease. org开发的大规模莱姆病患者登记处MyLymeData来调查这些问题。我们应用各种机器学习方法，以测量个体特征在预测参与者对全球变化评级（GROC）调查问题的回答中的作用，该调查问题评估了他们自我报告的病情改善，恶化，或在抗生素治疗后保持不变。我们使用基本的线性回归，支持向量机，神经网络，基于熵的决策树模型，和$k$ -最近邻方法。我们首先分析了模型的一般性能，然后确定预测参与者回答GROC的最重要的功能。在我们确定了“关键”特征之后，我们将它们从数据集中分离出来，并证明了这些特征在识别GROC方面的有效性。在这样做的时候，我们强调可能的方向，为未来的研究数学和临床。
英文摘要：
Lyme disease is a rapidly growing illness that remains poorly understood within the medical community. Critical questions about when and why patients respond to treatment or stay ill, what kinds of treatments are effective, and even how to properly diagnose the disease remain largely unanswered. We investigate these questions by applying machine learning techniques to a large scale Lyme disease patient registry, MyLymeData, developed by the nonprofit LymeDisease.org. We apply various machine learning methods in order to measure the effect of individual features in predicting participants' answers to the Global Rating of Change (GROC) survey questions that assess the self-reported degree to which their condition improved, worsened, or remained unchanged following antibiotic treatment. We use basic linear regression, support vector machines, neural networks, entropy-based decision tree models, and $k$-nearest neighbors approaches. We first analyze the general performance of the model and then identify the most important features for predicting participant answers to GROC. After we identify the "key" features, we separate them from the dataset and demonstrate the effectiveness of these features at identifying GROC. In doing so, we highlight possible directions for future study both mathematically and clinically.
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。
