title: A prediction model for patient classification according to nursing need: Using data mining techniques.
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%.
authors.affiliation: College of Nursing, Korea University, Korea. seomun@korea.ac.kr
authors.forename: Gyeong-Ae
authors.initials: GA
authors.lastname: Seomun
authors.forename: Sung Ok
authors.initials: SO
authors.lastname: Chang
authors.forename: Su Jeong
authors.initials: SJ
authors.lastname: Lee
authors.forename: In A
authors.initials: IA
authors.lastname: Kim
authors.forename: Sun-A
authors.initials: SA
authors.lastname: Park
mesh_terms: D000328:Adult; D000368:Aged; D003625:Data Collection / Q000379:methods; D005260:Female; D006301:Health Services Needs and Demand; D006801:Humans; D008297:Male; D009369:Neoplasms; D009732:Nursing Care; D010361:Patients / Q000145:classification*