统计学习技术应用于廉价的小儿阻塞性睡眠呼吸暂停数据的调查
A survey of statistical learning techniques as applied to inexpensive pediatric Obstructive Sleep Apnea data
摘要：
据估计，儿童阻塞性睡眠呼吸暂停影响1-5%的小学学龄儿童，并可能导致其他有害的健康问题。快速诊断和治疗对儿童的生长和发育至关重要，但症状的多变性和可用数据的复杂性使其成为一项挑战。我们通过关注来自问卷调查和颅面测量的廉价数据，迈出了简化过程的第一步。在探索性数据分析的过程中，我们应用了相关网络、拓扑数据分析中的Mapper算法和奇异值分解。然后，我们应用各种监督和无监督学习技术，从统计，机器学习和拓扑结构，从支持向量机到贝叶斯分类器和流形学习。最后，我们分析了这些方法的结果，并讨论了多数据源算法向前发展的影响。
英文摘要：
Pediatric obstructive sleep apnea affects an estimated 1-5% of elementary-school aged children and can lead to other detrimental health problems. Swift diagnosis and treatment are critical to a child's growth and development, but the variability of symptoms and the complexity of the available data make this a challenge. We take a first step in streamlining the process by focusing on inexpensive data from questionnaires and craniofacial measurements. We apply correlation networks, the Mapper algorithm from topological data analysis, and singular value decomposition in a process of exploratory data analysis. We then apply a variety of supervised and unsupervised learning techniques from statistics, machine learning, and topology, ranging from support vector machines to Bayesian classifiers and manifold learning. Finally, we analyze the results of each of these methods and discuss the implications for a multi-data-sourced algorithm moving forward.
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。
