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A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
Published on: January 12, 2018
Data mining methods find demographic predictors of preterm birth
L K Goodwin1, M A Iannacchione, W E Hammond
1Health Systems and Primary Care, and School of Nursing and Community and Family Health Medicine, Duke University, Durham, NC, USA. goodw010@mc.duke.edu
Insights
Predicting preterm birth is crucial. Simple demographic factors accurately identify at-risk pregnancies, showing data mining methods confirm traditional findings for preterm birth prediction.
Area of Science:
- Obstetrics and Gynecology
- Public Health
- Biostatistics
Background:
- Preterm birth rates in the U.S. present a persistent and complex healthcare challenge.
- The incidence of preterm births has shown an increasing trend in recent years.
Purpose of the Study:
- To compare the efficacy of traditional statistical methods against emerging data mining techniques.
- To identify accurate predictors for preterm birth using advanced analytical approaches.
Main Methods:
- Utilized an ethnically diverse dataset of 19,970 pregnant women with 1,622 variables.
- Applied both traditional statistical analyses and data mining methods to evaluate preterm birth predictors.
Main Results:
- Seven demographic variables achieved an area under the curve of 0.72 in predictive accuracy using Receiver Operating Characteristic curves.
- Incorporating hundreds of additional variables resulted in only a marginal improvement (0.03) in predictive accuracy.
Conclusions:
- Data mining methods consistently produced similar results, indicating findings are data-driven.
- A concise set of demographic variables demonstrates reasonable accuracy for predicting preterm birth outcomes in diverse populations.
Background:
Preterm births in the United States increased from 11.0% to 11.4% between 1996 and 1997; they continue to be a complex healthcare problem in the United States.
Objective:
The objective of this research was to compare traditional statistical methods with emerging new methods called data mining or knowledge discovery in databases in identifying accurate predictors of preterm births.
Method:
An ethnically diverse sample (N = 19,970) of pregnant women provided data (1,622 variables) for new methods of analysis. Preterm birth predictors were evaluated using traditional statistical and newer data mining analyses.
Results:
Seven demographic variables (maternal age and binary coding for county of residence, education, marital status, payer source, race, and religion) yielded a .72 area under the curve using Receiving Operating Characteristic curves to test predictive accuracy. The addition of hundreds of other variables added only a .03 to the area under the curve.
Conclusion:
Similar results across data mining methods suggest that results are data-driven and not method-dependent, and that demographic variables offer a small set of parsimonious variables with reasonable accuracy in predicting preterm birth outcomes in a racially diverse population.
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