Early Prediction of Preeclampsia via Machine Learning

Abstract Background Early prediction of preeclampsia is challenging due to poorly understood causes, various risk factors and likely multiple pathogenic phenotypes of preeclampsia. Statistical learning methods are well-equipped to deal with a large number of variables such as patients’ clinical and laboratory data, and to automatically select the most informative features. Objective Our objective was to use statistical learning methods to analyze all available clinical and laboratory data obtained during routine prenatal visits in early pregnancy and use them to develop a prediction model for preeclampsia. Study Design This was a retrospective cohort study that used data from 16,370 births at Lucile Packard Children Hospital at Stanford, California, from April 2014 to January 2018. Two statistical learning algorithms were used to build a predictive model: 1) Elastic net; 2) Gradient boosting algorithm. Models for all preeclampsia and early-onset preeclampsia ( Results Using the elastic net algorithm we developed a prediction model containing a subset of most informative features from all variables. The obtained prediction model for preeclampsia yielded an area under the curve of 0.79 (95% CI, 0.75, 0.83), sensitivity of 45.2% and false positive rate of 8.1%. The prediction model for early-onset preeclampsia achieved an area under the curve of 0.89 (95% CI 0.84, 0.95), true positive rate of 72.3% and false positive rate of 8.8%. Conclusion Statistical learning methods in a retrospective cohort study automatically identified a set of significant features for prediction and yielded high prediction performance for preeclampsia risk, from routine early pregnancy information.