Predicting Miscarriage and Stillbirth Using Weighted Ensemble Machine Learning [ID: 1338167]

Abstract

INTRODUCTION: Prediction of miscarriage and stillbirth remains a clinical challenge. Prior efforts to use machine learning tools have not used an ensemble weighted machine learning approach, called “super learner,” which offers the opportunity to improve prediction performance by aggregating the outputs of constituent machine learning models and preferentially weighting the highest-performing models. METHODS: Data were obtained from hospital-wide electronic health records from a large academic institution. The sample comprised 13,337 patients who delivered between 2008 and 2019, 6,932 of whom experienced a miscarriage or stillbirth. Phecodes for ICD-9-CM and ICD-10-CM were used to define miscarriage and stillbirth and create comorbidity categories. The constituent models of the super learner were XGBoost, random forest, a regularized generalized linear model (both lasso and ridge regressions), and a support vector machine. The objective of this study was to develop a super learner algorithm to predict miscarriage and stillbirth. RESULTS: The super learner model predicted miscarriage and stillbirth classification with an area under the receiver operating characteristic curve of 0.94 and an accuracy of 92%. It used two models: random forest, weighted at 73%, and SVM, weighted at 27%. The most highly weighted predictors were amniotic cavity abnormalities, pelvic soft tissue abnormalities, and preeclampsia. CONCLUSION: A super learner performs comparably to other models in the literature. External validation is warranted. The promising results suggest that a super learner model can be used as a clinical decision support tool, supplementing clinical judgement in predicting miscarriage and stillbirth.

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