Application of data mining techniques to determine patient satisfaction

Georgios Galatas, Dimitrios Zikos, Fillia Makedon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, we describe a novel methodology which employs machine learning as an alternative means to explore hospital characteristics and client satisfaction, for decision making and improved quality of care. We applied well known feature selection and data mining algorithms such as forward selection and Naïve Bayes respectively, to determine patient satisfaction, which is an important indicator of quality of care in hospital settings. Our dataset comprised of three types of data, (i) patient perception about received care, (ii) nurse perception about the working environment and (iii) organizational attributes of the hospital. Our experimental results exhibited high classification accuracy (87%), allowing valid conclusions to be reached about the organizational and workforce factors which attribute to patient satisfaction. Our findings were validated using traditional statistical methods such as binomial correlation and linear regression.

Original languageEnglish
Title of host publicationProceedings of PETRA 2013
Subtitle of host publicationThe 6th International Conference on PErvasive Technologies Related to Assistive Environments 2013
DOIs
StatePublished - 2013
Event6th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2013 - Rhodes, Greece
Duration: May 29 2013May 31 2013

Publication series

NameACM International Conference Proceeding Series

Conference

Conference6th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2013
Country/TerritoryGreece
CityRhodes
Period05/29/1305/31/13

Keywords

  • Naïve Bayes
  • feature selection
  • healthcare
  • machine learning
  • patient satisfaction

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