Predicting nurses' intention to quit with a Support Vector Machine: A new approach to set up an early warning mechanism in human resource management

Huey-Ming Tzeng, Jer Guang Hsieh, Yih Lon Lin

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This project developed a Support Vector Machine for predicting nurses' intention to quit, using working motivation, job satisfaction, and stress levels as predictors. This study was conducted in three hospitals located in southern Taiwan. The target population was all nurses (389 valid cases). For cross-validation, we randomly split cases into four groups of approximately equal sizes, and performed four training runs. After the training, the average percentage of misclassification on the training data was 0.86, while that on the testing data was 10.8, resulting in predictions with 89.2% accuracy. This Support Vector Machine can predict nurses' intention to quit, without asking these nurses whether they have an intention to quit.

Original languageEnglish (US)
Pages (from-to)232-242
Number of pages11
JournalCIN - Computers Informatics Nursing
Volume22
Issue number4
DOIs
StatePublished - Jan 1 2004
Externally publishedYes

Fingerprint

Nurses
Job Satisfaction
Health Services Needs and Demand
Taiwan
Motivation
Support Vector Machine

Keywords

  • Intention to quit
  • Job satisfaction
  • Nurse
  • Support Vector Machine
  • Working motivation

ASJC Scopus subject areas

  • Health Informatics
  • Nursing (miscellaneous)

Cite this

Predicting nurses' intention to quit with a Support Vector Machine : A new approach to set up an early warning mechanism in human resource management. / Tzeng, Huey-Ming; Hsieh, Jer Guang; Lin, Yih Lon.

In: CIN - Computers Informatics Nursing, Vol. 22, No. 4, 01.01.2004, p. 232-242.

Research output: Contribution to journalArticle

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