Next Article in Journal
A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network
Previous Article in Journal
Uniform vs. Nonuniform Membership for Mildly Context-Sensitive Languages: A Brief Survey
Article Menu

Export Article

Open AccessArticle

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

Department of Statistics, University of California, Riverside, CA 92521, USA
City of Hope Biostatistics Division, Duarte, CA 91010, USA
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
Algorithms 2016, 9(2), 33;
Received: 25 February 2016 / Revised: 25 April 2016 / Accepted: 2 May 2016 / Published: 13 May 2016
PDF [922 KB, uploaded 13 May 2016]


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

Figure 1

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).

Supplementary material


Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top