Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms

Abstract

BackgroundThe association between prenatal pesticide exposures and a higher incidence of small-for-gestational-age (SGA) births has been reported. No prediction model has been developed for SGA neonates in pregnant women exposed to pesticides prior to pregnancy.MethodsA retrospective cohort study was conducted using information from the National Free Preconception Health Examination Project between 2010 and 2012. A development set (n = 606) and a validation set (n = 151) of the dataset were split at random. Traditional logistic regression (LR) method and six machine learning classifiers were used to develop prediction models for SGA neonates. The Shapley Additive Explanation (SHAP) model was applied to determine the most influential variables that contributed to the outcome of the prediction.Results757 neonates in total were analyzed. SGA occurred in 12.9% (n = 98) of cases overall. With an area under the receiver-operating-characteristic curve (AUC) of 0.855 [95% confidence interval (CI): 0.752–0.959], the model based on category boosting (CatBoost) algorithm obtained the best performance in the validation set. With the exception of the LR model (AUC: 0.691, 95% CI: 0.554–0.828), all models had good AUCs. Using recursive feature elimination (RFE) approach to perform the feature selection, we included 15 variables in the final model based on CatBoost classifier, achieving the AUC of 0.811 (95% CI: 0.675–0.947).ConclusionsMachine learning algorithms can develop satisfactory tools for SGA prediction in mothers exposed to pesticides prior to pregnancy, which might become a tool to predict SGA neonates in the high-risk population.

The prediction of in-hospital mortality in elderly patients with sepsis-associated acute kidney injury utilizing machine learning models

“…In view of this, we believe that it is desirable and plausible that the MIMIC-IV database can be used to construct a model for predicting the hospitalized mortality in our research [ [20] , [21] , [22] ]. The validity of the RFE methodology has been demonstrated in a variety of medical studies [ [23] , [24] , [25] , [26] ], including the CatBoost model [ [27] , [28] , [29] ]. The CatBoost model demonstrated the best performance.…”

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy

“…A total of six algorithms were employed to improve the prediction models which had been described in our previous study, including LR, RF, GBDT, XGBoost, LGBM, and CatBoost [ 37 , 43 ]. Overall, traditional LR approach and other five methods are the most prevalent and state-of-the-art supervised machine learning approaches for categorization problems.…”