Authors:
Michael A. Pratt
and
Henry Chu
Affiliation:
University of Louisiana at Lafayette, United States
Keyword(s):
Predictive Analytics, Health System Analytics, Classifiers, Support Vector Machine, Random Forest.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Economics, Business and Forecasting Applications
;
Pattern Recognition
;
Theory and Methods
Abstract:
To make healthcare more cost effective, the current trend in the U.S. is towards a hospital value-based purchasing
program. In this program, a hospital’s performance is measured in the safety, patient experience of
care, clinical care, and efficiency and cost reduction domains. We investigate the efficacy of predicting the
safety measures using the patient experience of care measures. We compare four classifiers in the prediction
tasks and concluded that random forest and support vector machine provided the best performance.