Full
Outline
To construct and validate machine learning-based prediction models for the risk of excessive gestational weight gain(EGWG),and to compare the predictive performance of different models, and select the optimal one.
Using a convenience sampling method,a total of 623 puerpera who delivered at term and received prenatal check-ups at a tertiary grade A hospital in Beijing from December 2024 to June 2025 were selected as the study subjects.Univariate and multivariate Logistic regression were used to screen predictive factors.Prediction models were constructed by using six machine learning algorithms:Logistic regression,support vector machine,gradient boosting machine,neural network,extreme gradient boosting,and categorical boosting.Model performance was evaluated by using metrics,such as the area under the receiver operating characteristic curve(AUC),F1 score,accuracy,sensitivity,specificity,precision,and Brier Score to determine the optimal model,and a web calculator was developed.The SHapley additive explanations(SHAP) method was employed to interpret the contribution of each variable to the outcome in the best model.
Among 623 puerpera,198 (31.8%) cases experienced excessive gestational weight gain. Multivariate analysis results showed that pre-pregnancy body mass index,sedentary time during pregnancy,gestational diabetes mellitus,advanced maternal age,depressive symptoms during pregnancy,insufficient physical activity during pregnancy,and primiparity were risk factors for excessive gestational weight gain.Among the six machine learning models,the gradient boosting machine model demonstrated the best predictive performance(training set: AUC=0.836,F1=0.669;test set:AUC=0.756,F1=0.609).Calculation of SHAP values for the gradient boosting machine model revealed that pre-pregnancy body mass index,sedentary time during pregnancy,and gestational diabetes mellitus were the three key feature variables for predicting excessive gestational weight gain.
This study constructed a machine learning algorithm-based risk prediction model and web calculator for excessive gestational weight gain,providing an effective tool for early clinical identification and prevention.This contributes to the implementation of targeted clinical interventions and improves the effectiveness of gestational weight management.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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