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Algorithms 2016, 9(2), 33; doi:10.3390/a9020033

Mining Branching Rules from Past Survey Data with an Illustration Using a Geriatric Assessment Survey for Older Adults with Cancer

1
Department of Statistics, University of California, Riverside, CA 92521, USA
2
City of Hope Biostatistics Division, Duarte, CA 91010, USA
3
City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte 91010, CA, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Francesco Bergadano
Received: 25 February 2016 / Revised: 25 April 2016 / Accepted: 2 May 2016 / Published: 13 May 2016
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Abstract

We construct a fast data mining algorithm that can be used to identify high-frequency response patterns in historical surveys. Identification of these patterns leads to the derivation of question branching rules that shorten the time required to complete a survey. The data mining algorithm allows the user to control the error rate that is incurred through the use of implied answers that go along with each branching rule. The context considered is binary response questions, which can be obtained from multi-level response questions through dichotomization. The algorithm is illustrated by the analysis of four sections of a geriatric assessment survey used by oncologists. Reductions in the number of questions that need to be asked in these four sections range from 33% to 54%. View Full-Text
Keywords: branching rules; historical surveys; data mining branching rules; historical surveys; data mining
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jeske, D.R.; Longmate, J.; Katheria, V.; Hurria, A. Mining Branching Rules from Past Survey Data with an Illustration Using a Geriatric Assessment Survey for Older Adults with Cancer. Algorithms 2016, 9, 33.

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