# Feature Selection from Lyme Disease Patient Survey Using Machine Learning
## 2 Data and Methods
### The Dataset  
We use data from Phase 1 of the MyLymeData patient registry, a private Lyme disease patient survey data set. Participants include respondents who report being US residents diagnosed with Lyme disease. We look only at participants who satisfy all of the following criteria:  
1. Participant has persistent Lyme disease, which consists of patients who have experienced persistent symptoms for at least six months after antibiotic treatment. 2. Participant responded that they were unwell. 3. Participant answered the GROC (Global Rating of Change) survey questions. We assign each participant a label based on their response to GROC, as previously described in [10]. As asked, the GROC question produces a 17-point Likert scale. It is a two-part question asking first if the patient is \"better\", \"worse\", or \"unchanged\". Patients who responded that they were better or worse are asked to specify the degree of improvement ranging from \"almost the same\" to \"a very great deal better/worse\". \"Almost the same\" responses for better or worse were combined with the unchanged response. As modified, the resultant 15 point Likert scale ranges between \(-7\) and \(7\), with \(0\) as the midpoint for unchanged. We separate participants into three categories:  
1. Non-responders, who answered between \(-7\) and \(0\), indicating there was no improvement. 2. Low responders, who answered between \(1\) and \(3\), indicating there was slight improvement. 3. High responders, who answered between \(4\) and \(7\), indicating there was substantial improvement. The dataset consists of 2162 participants who satisfy the necessary criteria and 215 features (question responses) drawn from the MyLymeData survey. Each participant is assigned a label indicating non-responder, low responder, or high responder. The dataset has a total of 947 non-responders, 396 low responders, and 819 high responders. In our experiments, we investigate only the non-responders and high-responders. We choose not to include the low responders in our study because of the small number of low responders, and because this middle group could make it difficult to identify features that separate the non-responders from high responders. We also subsample from the dataset in order to produce an evenly sized groups of non-responders and high responders, and use this subsample for all experiments. As we describe in Sections 2.3 and 2.5, we randomize this split repeatedly over many trials in order to capture all the information from the dataset. The 215 features cover diagnostic factors (such as delays to diagnosis, stage of diagnosis, or presence of coinfections), treatment approach, individual antibiotic use and duration of use, alternative treatments, symptoms (severity, presence at time of diagnosis, and three worst),type of clinician, and degree of functional impairment. We refer to this dataset as MLD (MyLymeData), and we refer to all participants with a specific label as a class of participants. In order to improve the survey-taking experience for participants, the format of the survey used a branching structure to direct only relevant questions to participants. Thus, for many of the features in our analysis, only a subset of participants provided a response. For every such feature, we group all participants who did not respond to the feature together with a unique response. For our figures and tables, we provide abbreviations of the relevant features. A name with a number following it indicates a series of questions within the same subtopic, and here we note such features with an \"1\". \"Bio_Sex\" indicates biological sex. \"Sx_Dx_i\" indicates symptoms present at diagnosis. \"Tick\" indicates the presence of a tick bite. \"Sx_Sev_i\" indicates the severity of specific symptoms. \"Sx_Top_i\" indicates a specific symptom as being in the top 3 symptoms. \"Abx\" indicates whether the participant is currently taking antibiotics and/or alternative treatments. \"Abx_Not_i\" indicates the reasons that a participant is currently not taking antibiotics.