  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.
