Investigating Risk Factors Affecting Infant Mortality Rates in the United States

By Chau-Kuang Chen.

Published by The Technology Collection

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Article: Print $US10.00
Article: Electronic $US5.00

Support Vector Machine (SVM) and Artificial Neural Network (ANN) approaches have been widely used for pattern recognition and data prediction. These approaches can be used to establish nonlinear relationships among variables as well as to perform variable ranking and selection. The purpose of this study was to construct workable SVM and ANN models to assess risk factors affecting infant mortality rates in the United States (U.S.). Data was collected in years 2000-2006 at the state level from the U.S. Census Bureau, the Center for Disease Control and Prevention, and the U.S. Departments of Labor and Commerce. The outcome variable was infant mortality rate, and risk factors included per capita income, unemployment rate, teen pregnancy rate, percent of teen mothers who smoke, percent of newborns with gestation stage less than 37 weeks, percent of newborns who weigh less than 2500 grams, and many more. The top seven important risk factors found in the SVM and ANN models were consistent with the literature findings, which are major contributors of the infant mortality rate. It is evident that SVM and ANN models have successfully demonstrated their model validity and applicability. Therefore, the study results should be used to help policy makers implement intervention strategies needed to reduce infant mortality rate.

Keywords: Artificial Neural Network, Support Vector Machine, Infant Mortality

International Journal of Technology, Knowledge and Society, Volume 7, Issue 4, pp.119-128. Article: Print (Spiral Bound). Article: Electronic (PDF File; 742.035KB).

Dr. Chau-Kuang Chen

Associate Professor and Director of Institutional Research, Office of Institutional Research, Meharry Medical College, Nashville, Tennessee, USA

Dr. Chau-Kuang Chen has taught Biostatistics to graduate students and medical residents at Meharry Medical College for more than 20 years, specializing in generalized linear model, survival analysis, time series analysis, and artificial intelligence modeling approach. He was one of the first to incorporate a variety of sophisticated techniques--ordered logit/cloglog, proportional hazard, transfer function of ARIMA methodology, grey forecasting model, and artificial intelligence methods--into higher education processes and outcomes. Dr. Chen has won the 2008 Distinguished Graduate Educator Award, and the Dean’s Award for Excellence in Teaching in the School of Graduate Studies three times, conducted numerous statistical workshops at annual conferences of the Association for Institutional Research (AIR), presented statistical seminar at the University of Oxford, UK, and published several articles in the Web-based IR Applications, an AIR refereed journal.


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