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Article

Feature Selection from Lyme Disease Patient Survey Using Machine Learning

1
Department of Mathematics, University of California, Los Angeles, CA 90095, USA
2
Chief Executive Officer, LymeDisease.org, Los Angeles, CA 94583, USA
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(12), 334; https://doi.org/10.3390/a13120334
Received: 10 November 2020 / Revised: 1 December 2020 / Accepted: 9 December 2020 / Published: 11 December 2020
(This article belongs to the Special Issue Machine Learning Algorithms for Biomedical Signal Processing)
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. View Full-Text
Keywords: Lyme disease; machine learning; feature selection; survey data; symptom severity Lyme disease; machine learning; feature selection; survey data; symptom severity
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MDPI and ACS Style

Vendrow, J.; Haddock, J.; Needell, D.; Johnson, L. Feature Selection from Lyme Disease Patient Survey Using Machine Learning. Algorithms 2020, 13, 334. https://doi.org/10.3390/a13120334

AMA Style

Vendrow J, Haddock J, Needell D, Johnson L. Feature Selection from Lyme Disease Patient Survey Using Machine Learning. Algorithms. 2020; 13(12):334. https://doi.org/10.3390/a13120334

Chicago/Turabian Style

Vendrow, Joshua; Haddock, Jamie; Needell, Deanna; Johnson, Lorraine. 2020. "Feature Selection from Lyme Disease Patient Survey Using Machine Learning" Algorithms 13, no. 12: 334. https://doi.org/10.3390/a13120334

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