Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Dec 13, 2022
Open Peer Review Period: Dec 13, 2022 - Jan 3, 2023
Date Accepted: Jun 18, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Prediction of Fetal Alcohol Spectrum Disorders Using Machine Learning Algorithms: Algorithm Development
ABSTRACT
Background:
Fetal Alcohol Syndrome (FAS) is a lifelong developmental disability which occurs among individuals with prenatal alcohol exposure. With improved prediction models, FAS can be diagnosed/treated early, if not completely prevented.
Objective:
In this study, we sought to compare different machine learning algorithms and their FAS predictive performance among women who consumed alcohol during pregnancy. We also aimed to identify which variables (e.g., timing of exposure to alcohol during pregnancy, type of alcohol consumed, etc.) were most influential in generating an accurate model.
Methods:
Data from the Collaborative Initiative on Fetal Alcohol Spectrum Disorders (CIFASD) from 2007-2017 were used to gather information about 595 women who consumed alcohol during pregnancy at five hospital sites around the United States. Four different machine learning algorithms were trained to predict the prevalence of FAS at birth, and model performance was measured by analyzing the area under the receiver operating characteristics curve (AUROC). Feature importance was also analyzed using Shapley Values for the best-performing algorithm.
Results:
Best performance was detected in the CatBoost algorithm, which had an AUROC of 0.92 and an AUPRC of 0.45. In this model, 12 variables were considered important in FAS prediction, with drinking throughout all three trimesters of pregnancy, maternal age, race, and type of alcoholic beverage consumed (e.g., beer, wine, or liquor) scoring highly in overall feature importance.
Conclusions:
Machine learning algorithms were able to identify FAS risk with a prediction performance higher than that of previous models, among pregnant drinkers. Using models like CatBoost may help physicians identify high-risk populations and predict/prevent FAS in a timely manner. Clinical Trial: Institutional Review Boards (IRBs) at all CIFASD sites approved this study, and the Harvard T.H. Chan School of Public Health IRB approved analyses of these secondary data (Protocol #: IRB21-1261).
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Copyright
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