Using Data Mining Technique to Study the Likelihood of Dental Students from a Disadvantaged Background Practicing in a Disadvantaged Community

Background: Studies have shown that although the number of dentist serving the underserved has increased, it is still insufficient.  The objective of this study was to determine if dental school matriculants from a disadvantaged background are likely to practice dentistry in a disadvantaged community. Dental students are motivated to serve the underserved community.   

Methodology: The dataset consisted of 213 dental students from 2006-2009 matriculation years and of these students, 188 graduated in 2010-2013. The geographical information system (GIS) was used to extract variables (e.g., median household income, household size) within the census tracts which are linked with the addresses of parents’ home and dentist practicing location. K-means cluster analysis was used to classify the students into three groups, disadvantaged, moderately disadvantaged, and non-disadvantaged. The means of median household income are for disadvantaged group- $33,000, moderately disadvantaged group- $62,000, and non-disadvantaged group- $112,000. The means of unemployment rates for the three groups are 13%, 8.6%, and 6%, respectively. Decision tree analysis was used as a benchmarking tool to verify the accuracy of classification. Partial least square (PLS) regression was implemented to demonstrate prediction validity of clustering variables.  

Results and Conclusion: Over 90% of students from a disadvantaged background, 88% of students from a moderately disadvantaged background, and 52% of students from a non-disadvantaged background are practicing dentistry in a disadvantaged community. The vast majority of students who have graduated practice in a disadvantaged community, indicating that they significantly contribute to the College’s mission in serving underserved community and reducing health disparities.