The Univariate Flagging Algorithm (UFA): An interpretable approach for predictive modeling


Autoři: Mallory Sheth aff001;  Albert Gerovitch aff001;  Roy Welsch aff001;  Natasha Markuzon aff002
Působiště autorů: Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America aff001;  The Charles Stark Draper Laboratory, Cambridge, Massachusetts, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223161

Souhrn

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.

Klíčová slova:

Algorithms – Body temperature – Death rates – Machine learning – Machine learning algorithms – Medical doctors – Sepsis – Support vector machines


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PLOS One


2019 Číslo 10

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