The Univariate Flagging Algorithm (UFA): An interpretable approach for predictive modeling
Name
journal.pone.0223161.pdf
Description
Published version
Size
1.34 MB
Format
Adobe PDF
Checksum (MD5)
61b37fb2253d57b4cbc0fdd4c806c754
Author(s)
Sheth, Mallory
Gerovitch, Albert S.
Welsch, Roy E.
Date Issued
October 11, 2019
Journal
PloS one
Publisher
Public Library of Science (PLoS)
Citation
Sheth, Mallory et al. "The Univariate Flagging Algorithm (UFA): An interpretable approach for predictive modeling." PloS one 14 (2019)
Version
Final published version
Abstract
In many data classification problems, a number of methods will give similar accuracy. However, when working with people who are not experts in data science such as doctors, lawyers, and judges among others, finding interpretable algorithms can be a critical success factor. Practitioners have a deep understanding of the individual input variables but far less insight into how they interact with each other. For example, there may be ranges of an input variable for which the observed outcome is significantly more or less likely. This paper describes an algorithm for automatic detection of such thresholds, called the Univariate Flagging Algorithm (UFA). The algorithm searches for a separation that optimizes the difference between separated areas while obtaining a high level of support. We evaluate its performance using six sample datasets and demonstrate that thresholds identified by the algorithm align well with published results and known physiological boundaries. We also introduce two classification approaches that use UFA and show that the performance attained on unseen test data is comparable to or better than traditional classifiers when confidence intervals are considered. We identify conditions under which UFA performs well, including applications with large amounts of missing or noisy data, applications with a large number of inputs relative to observations, and applications where incidence of the target is low. We argue that ease of explanation of the results, robustness to missing data and noise, and detection of low incidence adverse outcomes are desirable features for clinical applications that can be achieved with relatively simple classifier, like UFA.
Subjects
General Biochemistry, Genetics and Molecular Biology
General Agricultural and Biological Sciences
General Medicine
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0223161
