Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study

特征选择 随机森林 机器学习 支持向量机 人工智能 产后抑郁症 排名(信息检索) 计算机科学 预测建模 医学 怀孕 遗传学 生物
作者
Weina Zhang,Han Liu,Vincent Silenzio,Peiyuan Qiu,Wenjie Gong
出处
期刊:JMIR medical informatics [JMIR Publications]
卷期号:8 (4): e15516-e15516 被引量:58
标识
DOI:10.2196/15516
摘要

Postpartum depression (PPD) is a serious public health problem. Building a predictive model for PPD using data during pregnancy can facilitate earlier identification and intervention.The aims of this study are to compare the effects of four different machine learning models using data during pregnancy to predict PPD and explore which factors in the model are the most important for PPD prediction.Information on the pregnancy period from a cohort of 508 women, including demographics, social environmental factors, and mental health, was used as predictors in the models. The Edinburgh Postnatal Depression Scale score within 42 days after delivery was used as the outcome indicator. Using two feature selection methods (expert consultation and random forest-based filter feature selection [FFS-RF]) and two algorithms (support vector machine [SVM] and random forest [RF]), we developed four different machine learning PPD prediction models and compared their prediction effects.There was no significant difference in the effectiveness of the two feature selection methods in terms of model prediction performance, but 10 fewer factors were selected with the FFS-RF than with the expert consultation method. The model based on SVM and FFS-RF had the best prediction effects (sensitivity=0.69, area under the curve=0.78). In the feature importance ranking output by the RF algorithm, psychological elasticity, depression during the third trimester, and income level were the most important predictors.In contrast to the expert consultation method, FFS-RF was important in dimension reduction. When the sample size is small, the SVM algorithm is suitable for predicting PPD. In the prevention of PPD, more attention should be paid to the psychological resilience of mothers.
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