TY - JOUR
T1 - Predicting nurses' intention to quit with a Support Vector Machine
T2 - A new approach to set up an early warning mechanism in human resource management
AU - Tzeng, Huey-Ming
AU - Hsieh, Jer Guang
AU - Lin, Yih Lon
PY - 2004/1/1
Y1 - 2004/1/1
N2 - 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.
AB - 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.
KW - Intention to quit
KW - Job satisfaction
KW - Nurse
KW - Support Vector Machine
KW - Working motivation
UR - http://www.scopus.com/inward/record.url?scp=7644238350&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=7644238350&partnerID=8YFLogxK
U2 - 10.1097/00024665-200407000-00012
DO - 10.1097/00024665-200407000-00012
M3 - Article
C2 - 15494654
AN - SCOPUS:7644238350
SN - 1538-2931
VL - 22
SP - 232
EP - 242
JO - CIN - Computers Informatics Nursing
JF - CIN - Computers Informatics Nursing
IS - 4
ER -