Abstract

BackgroundPostpartum depression (PPD) is a serious public health problem. Building a predictive model for PPD using data during pregnancy can facilitate earlier identification and intervention.ObjectiveThe 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.MethodsInformation 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.ResultsThere 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.ConclusionsIn 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.

Highlights

  • Postpartum depression (PPD) is a serious public health problem that affects 10% to 20% of pregnant women [1,2,3]

  • In contrast to the expert consultation method, random forest-based filter feature selection Generalized Anxiety Disorder-7 (GAD-7) (FFS-random forest (RF)) was important in dimension reduction

  • When the sample size is small, the support vector machine (SVM) algorithm is suitable for predicting PPD

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Summary

Introduction

Postpartum depression (PPD) is a serious public health problem that affects 10% to 20% of pregnant women [1,2,3]. PPD adversely affects the physical and mental health of mothers, it is detrimental to the growth and development of infants. Establishing an effective PPD prediction model that can be used in pregnancy may enable earlier identification, helping http://medinform.jmir.org/2020/4/e15516/ XSLFO RenderX. 1 (page number not for citation purposes) health care providers offer more effective management to at-risk The study was approved by the institutional review board of patients [5]. 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

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