2020/08/24 by Joshua Vendrow, Jamie Haddock, Vendrow, Joshua +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Genetic and phenotypic traits in livestock #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods in Epidemiology #cs.CY #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2009.09087
9 pages, 8 figures, 6 tables
arxiv created 2020/08/24 · openalex publication_date 2020/08/24 · arxiv updated 2020/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.