Feature Selection on Lyme Disease Patient Survey Data

TL;DR

This study applies various machine learning techniques to Lyme disease patient survey data to identify key features influencing treatment response and disease progression, aiming to improve understanding and diagnosis.

Contribution

It introduces a comprehensive machine learning approach to feature selection in Lyme disease patient data, highlighting important predictors of treatment outcomes.

Findings

Key features identified for predicting patient responses

Machine learning models effectively distinguish important clinical factors

Potential directions for future mathematical and clinical research

Abstract

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.