221337 Investigating Risk Factors Affecting the United States Teenage Pregnancy Rates based on Support Vector Machine Classifier

Tuesday, November 9, 2010

Chau-Kuang Chen, EDD , Institutional Research, Meharry Medical College, Nashville, TN
Piia Hanson, BA , School of Graduate Studies, Meharry Medical College, Nashville, TN
Naa Ayele Amponsah, BA , School of Graduate Studies, Meharry Medical College, Nashville, TN
Teenage pregnancy remains an important issue in the United States. After several years of decline, the teenage pregnancy rate is rising with a birth rate of 41.9 per 1,000,000 live births for mothers between the ages of 15 and 19 (Martin, 2007). The purpose of this study is to investigate important risk factors affecting the teenage pregnancy rate. Through a review of the literature we were able to identify important factors including state per capita income, state unemployment rate, state poverty rate, percent of state population with less than nine years of education, and percent of state population with a high school diploma. The data used for this study comes from the U.S. Department of Labor and was representative of fifty states between the years 2000 and 2006. Support vector machine was used to establish the nonlinear relationship between teenage pregnancy rate and the risk factors. The SVM classifier maps input data from the input space into the high-dimensional feature space, and seeks an optimal hyperplane to separate data from opposite multiple classes. The results show that the polynomial SVM model yields the most accurate results with an R-squared value of 0.70. The top three ranking contributors to the teenage pregnancy rate are state per capita income, state poverty rate, and percent of state population who entered high school but did not receive a diploma. This finding highlights the need for prevention programs among these high risk populations and the role socioeconomic status and education play in teenage pregnancy.

Learning Areas:
Biostatistics, economics
Public health or related education
Public health or related public policy

Learning Objectives:
) To familiarize the audience with the Support Vector Machine Classifier. 2) To explain the important variables affecting the U.S. teenage pregnancy rate. 3) To demonstrate the practical uses of the Support Vector Machine Classifier.

Keywords: Teen Pregnancy, Risk Factors

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am qualified to be an abstract author on the content becuase I have conducted the data analysis for this study.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

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