A prediction model for patient classification according to nursing need: Using data mining techniques

Gyeong Ae Seomun, Sung Ok Chang, Su Jeong Lee, In A. Kim, Sun A. Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The purpose of this study was to construct a prediction model for patient classification according to nursing need. The results were assessed from the classification of the hospitalized cancer patients by three different data mining techniques: logistic regression, decision tree and neural network. Among these three techniques, neural network showed the best prediction power in ROC curve verification. The prediction model for patient classification developed by neural network based on nurse needs produced a prediction accuracy of 84.06%.

Original languageEnglish
Title of host publicationConsumer-Centered Computer-Supported Care for Healthy People - Proceedings of NI 2006
Subtitle of host publicationThe 9th International Congress on Nursing Informatics
PublisherIOS Press
Number of pages1
ISBN (Print)158603622X, 9781586036225
Publication statusPublished - 2006 Jan 1
Event9th International Congress on Nursing Informatics, NI 2006 - Seoul, Korea, Republic of
Duration: 2006 Jun 92006 Jun 21

Publication series

NameStudies in Health Technology and Informatics
Volume122
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other9th International Congress on Nursing Informatics, NI 2006
CountryKorea, Republic of
CitySeoul
Period06/6/906/6/21

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Keywords

  • Cancer
  • Classification
  • Data
  • Model
  • Nursing

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Seomun, G. A., Chang, S. O., Lee, S. J., Kim, I. A., & Park, S. A. (2006). A prediction model for patient classification according to nursing need: Using data mining techniques. In Consumer-Centered Computer-Supported Care for Healthy People - Proceedings of NI 2006: The 9th International Congress on Nursing Informatics (Studies in Health Technology and Informatics; Vol. 122). IOS Press.