Machine learning for an expert system to predict preterm birth risk

Abstract

Objective: Develop a prototype expert system for preterm birth risk assessment of pregnant women. Normal gestation involves a term of 40 weeks, but because 8-12% of the newborns in the United States are delivered prior to 37 weeks' gestation, problems associated with prematurity continue to plague individuals, families, and the health care system.

Design: A knowledge-base development methodology used machine learning, statistical analysis, and validation techniques to analyze three large datasets (18,890 subjects and 214 variables). The dependent (i.e., decision) variable studied was weeks of gestation at delivery, with dichotomous coding of preterm delivery (prior to 37 weeks) and full-term delivery (37+ weeks).

Results: Machine learning with a program named Learning from Examples using Rough Sets (LERS) induced 520 usable rules that were entered into a prototype expert system. The prototype expert system was 53-88% accurate in predicting preterm delivery for 9,419 patients.

Conclusion: The prototype expert system was more accurate than traditional manual techniques in predicting preterm birth.