Abstract
Determining the factors that contribute to the healthcare disparity in United States is a substantial problem that healthcare professionals have confronted for decades. In this study, our objective is to build precise and accurate classification models to predict the factors, which attribute to the disparity in healthcare coverage in the United States. The study utilizes twenty-three variables and 67,636 records from the 2007 Behavioral Risk Factor Surveillance System (BRFSS). In our comparative analysis, three statistical feature extraction methods, Chi-Square, Gain Ratio, and Info Gain, were used to extract a set of relevant features, which were then subjected to the classification models, AdaBoost, Random Forest, Radial Basis Function (RBF), Logistic Regression, and Naïve Bayes, to analyze healthcare coverage. The most important factors that were discovered in the model are presented in this paper.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Carrasquillo, O., Carrasquillo, A.I., Shea, S.: Health Insurance Coverage of Immigrants Living in the United States: Differences by Citizenship Status and Country of Origin. American Journal of Public Health 90, 917–923 (2000)
Hendryx, M.S., Ahern, M.M., Lovrich, N.P., McCurdy, A.H.: Access to Health Care and Community Social Capital. Health Services Research 37, 87–103 (2002)
Monheit, A.C., Vistnes, J.P.: Race/Ethnicity and Health Insurance Status: 1987 and 1996. Medical Care Research and Review 57, 11–35 (2000)
Delen, D., Fuller, C., McCann, C., Ray, D.: Analysis of Healthcare Coverage: A Data Mining Approach. Expert Systems with Applications 36, 995–1003 (2009)
Glover, S., Moore, C.G., Probst, J.C., Samuels, M.E.: Disparities in Access to Care among Rural Working-Age Adults. Journal of Rural Health 20, 193–205 (2004)
Lucas, J.W., Barr-Anderson, D.J., Kington, R.S.: Health Status, Health Insurance, and Health Care Utilization Patterns of Immigrant Black Men. American Journal of Public Health 93, 1740–1747 (2003)
Shi, L.Y.: Vulnerable Populations and Health Insurance. Medical Care Research and Review 57, 110–134 (2000)
Cardon, J.H., Hendel, I.: Asymmetric Information in Health Insurance: Evidence from the National Medical Expenditure Survey. Rand Journal of Economics 32, 408–427 (2001)
Anderson, S.G., Eamon, M.K.: Stability of Health Care Coverage among Low-Income Working Women. Health and Social Work 30, 7–17 (2005)
Cawley, J., Simon, K.I.: Health Insurance Coverage and the Macroeconomy. Journal of Health Economics 24, 299–315 (2005)
Rowland, D.: Health Care and Medicaid- Weathering the Recession. New England Journal of Medicine 360, 1273–1276 (2009)
Carrasquillo, O., Himmelstein, D.U., Woolhandler, S., Bor, D.H.: Going bare: Trends in Health Insurance Coverage, 1989 through 1996. American Journal of Public Health 89, 36–42 (1999)
Landerman, L.R., Fillenbaum, G.G., Pieper, C.F., Maddox, G.L., Gold, D.T., Guralnik, J.M.: Private Health Insurance Coverage and Disability among Older Americans. Journals of Gerontology Series B-Psychological Sciences And Social Sciences 53, S258–S266 (1998)
Hoffman, C., Schlobohm, A.: Uninsured in America: A Chart Book, 2nd edn. The Henry J. Kaiser Family Foundation, Washington (2000)
Newacheck, P.W., Park, M.J., Brindis, C.D., Biehl, M., Irwin, C.E.: Trends in Private and Public Health Insurance for Adolescents. The Journal of American Medical Association 291, 1231–1237 (2004)
Woolhandler, S., Himmelstein, D.U., Distajo, R., Lasser, K.E., McCormick, D., Bor, D.H., et al.: America’s Neglected Veterans: 1.7 Million Who Served Have No Health Coverage. International Journal of Health Services 35, 313–323 (2005)
Chae, Y.M., et al.: Data Mining Approach to Policy Analysis in a Health Insurance Domain. International Journal of Medical Informatics 62, 103–111 (2001)
Cunningham, P.J., Ginsburg, P.B.: What Accounts for Differences in Uninsurance Rates across Communities? Inquiry-The Journal of Health Care Organization Provision and Financing 38, 6–21 (2001)
Leigh, J., Hubert, H., Romano, P.: Lifestyle Risk Factors Predict Healthcare Costs in an Aging Cohort. American Journal of Preventive Medicine 29, 379–387 (2005)
Wilson, R.L., Sharda, R.: Bankruptcy Prediction using Neural Networks. Decision Support Systems 11, 545–557 (1994)
Rish, I.: An Empirical Study of the Naive Bayes Classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence (2001)
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Hilbe, J.M.: Logistic Regression Models. Chapman & Hall/CRC Press, Boca Raton (2009)
Grove, A., Schuurmans, D.: Boosting in the Limit: Maximizing the Margin of Learned Ensembles. In: Proceedings of the 15th National Conf. Artificial Intelligence, pp. 692–699 (1998)
Jin, R., Liu, Y., Si, L., Carbonell, J., Hauptmann, A.: A New Boosting Algorithm Using Input-Dependent Regularizer. In: Proceedings of 20th Intl. Conf. on Machine Learning, ICML 2003 (2003)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford Univ. Press, Oxford (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sethi, P., Jain, M. (2010). A Comparative Feature Selection Approach for the Prediction of Healthcare Coverage. In: Prasad, S.K., Vin, H.M., Sahni, S., Jaiswal, M.P., Thipakorn, B. (eds) Information Systems, Technology and Management. ICISTM 2010. Communications in Computer and Information Science, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12035-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-12035-0_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12034-3
Online ISBN: 978-3-642-12035-0
eBook Packages: Computer ScienceComputer Science (R0)