# Feature Selection from Lyme Disease Patient Survey Using Machine Learning
## 1 Introduction  
Lyme disease is the most common vector-borne disease in the United States. The CDC estimates that 300,000 people in the U.S. (approximately 1% of the population) are diagnosed with Lyme Disease each year [1], a rate 1.5 times higher than breast cancer [2], and six times higher than HIV/AIDS [3]. In its early, or acute, form, the disease may cause a hallmark erythema migrans (EM) rash and/or flu-like symptoms such as fever, malaise, fatigue, and generalized achiness [4]. A significant proportion of patients with Lyme disease develop chronic debilitating symptoms that persist in the absence of initial treatment or following short-course antibiotic therapy [5]. This condition is commonly referred to as post-treatment Lyme disease or as chronic or persistent Lyme disease. In this paper, we refer to these patients as having persistent Lyme disease. It is estimated that as many as 36% of those diagnosed and treated early remain ill after treatment [5]. However, despite the high incidence and severity of Lyme disease, little research has been done, both clinically and analytically [6; 7; 8; 9]. The result has been a stagnant and controversial research environment with little innovation and a costly lack of understanding or consensus. Physicians still do not know the best way to diagnose or treat Lyme, how it progresses, or why some patients respond to treatment and others do not. **Motivating questions. ** We are motivated by questions that interest both physicians and patients to better inform treatment approaches and to identify factors that might predict treatment response. **MyLymeData. ** Founded over 30 years ago, LymeDisease.org (LDo) is a national 501(c)(3) non-profit dedicated to advocacy, research, and education. LDo has conducted surveys with the Lyme disease patient community since 2004, and published the results in peer reviewed journals. In November 2015, LDo launched MyLymeData, a patient registry. MyLymeData has enrolled over 13,000 patients and continues to grow. Participants are asked hundreds of questions regarding their health history, diagnosis, symptoms, and treatment. The first study using data from the registry was published in 2018. That study focused on treatment response variation among patients and identified a subgroup of high treatment responders using the Global Rating of Change Scale (GROC), a widely used and highly validated treatment response measurement [10]. The GROC survey questions assess the degree to which participants reported that their condition improved, worsened, or remained unchanged following antibiotic treatment. We assign participants to class labels based on their GROC responder status. We label each participant as a high responder if they experienced substantial improvement, a low responder if they experienced slight improvement, and a non-responder if they worsened or remained unchanged. Medically, a major goal is to understand what patient attributes, protocols, or circumstances lead to patient improvement. **Machine learning techniques. ** One challenge facing medical experts looking to derive insights from the data are that the high-dimensional structure of the data obscures the relationship between patient features and their GROC responder status. In this work, we apply various machine learning models in order to measure the efficacy of individual features (survey question responses) in classifying the patients' GROC responder status, and to identify both meaningful and redundant information within the survey responses. We apply simple _wrapper_ approaches to feature selection [11]. _We aggregate the results of several approaches to select a final subset of features we find most relevant to patients' GROC responder status, thereby highlighting what patient attributes and protocols are most likely associated with improved patient well being. These findings point to areas where additional analysis might prove useful._  
**Related work.