Predicting Voluntary Participation in a Public Health Program Using a Neural Network

Predicting Voluntary Participation in a Public Health Program Using a Neural Network

George E. Heilman (Winston-Salem State University, USA), Monica Cain (Winston-Salem State University, USA) and Russell S. Morton (Winston-Salem State University, USA)
DOI: 10.4018/jhisi.2008040101
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Abstract

Researchers increasingly use Artificial Neural Networks (ANNs) to predict outcomes across a broad range of applications. They frequently find the predictive power of ANNs to be as good as or better than conventional discrete choice models. This article demonstrates the use of an ANN to model a consumer’s choice to participate in North Carolina’s Maternity Care Coordination (MCC) program, a state sponsored voluntary public health service initiative. Maternal and infant Medicaid claims data and birth certificate data were collected for 59,999 births in North Carolina during the years 2000-2002. Part of this sample was used to train and test an ANN that predicts voluntary enrollment in MCC. When tested against a holdout production sample, the ANN model correctly predicted 99.69% of those choosing to participate and 100% of those choosing not to participate in the MCC program.

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