  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.
