Machine Learning-Based Predictive Modeling of Postpartum Depression
1
Department of Food and Nutrition, Inha University, Incheon 22212, Korea
2
Department of Obstetrics and Gynecology, Korea University Medical Center, Seoul 02841, Korea
3
Department of Biomedical Sciences, University of North Dakota, Grand Forks, ND 58202, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to the work.
J. Clin. Med. 2020, 9(9), 2899; https://doi.org/10.3390/jcm9092899
Received: 22 August 2020
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Revised: 6 September 2020
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Accepted: 7 September 2020
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Published: 8 September 2020
(This article belongs to the Special Issue Pregnancy and Bipolar Disorder)
Postpartum depression is a serious health issue beyond the mental health problems that affect mothers after childbirth. There are no predictive tools available to screen postpartum depression that also allow early interventions. We aimed to develop predictive models for postpartum depression using machine learning (ML) approaches. We performed a retrospective cohort study using data from the Pregnancy Risk Assessment Monitoring System 2012–2013 with 28,755 records (3339 postpartum depression and 25,416 normal cases). The imbalance between the two groups was addressed by a balanced resampling using both random down-sampling and the synthetic minority over-sampling technique. Nine different ML algorithms, including random forest (RF), stochastic gradient boosting, support vector machines (SVM), recursive partitioning and regression trees, naïve Bayes, k-nearest neighbor (kNN), logistic regression, and neural network, were employed with 10-fold cross-validation to evaluate the models. The overall classification accuracies of the nine models ranged from 0.650 (kNN) to 0.791 (RF). The RF method achieved the highest area under the receiver-operating-characteristic curve (AUC) value of 0.884, followed by SVM, which achieved the second-best performance with an AUC value of 0.864. Predictive modeling developed using ML-approaches may thus be used as a prediction (screening) tool for postpartum depression in future studies.
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Keywords:
postpartum depression; machine learning; predictive modeling; Pregnancy Risk Assessment Monitoring System (PRAMS)
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MDPI and ACS Style
Shin, D.; Lee, K.J.; Adeluwa, T.; Hur, J. Machine Learning-Based Predictive Modeling of Postpartum Depression. J. Clin. Med. 2020, 9, 2899. https://doi.org/10.3390/jcm9092899
AMA Style
Shin D, Lee KJ, Adeluwa T, Hur J. Machine Learning-Based Predictive Modeling of Postpartum Depression. Journal of Clinical Medicine. 2020; 9(9):2899. https://doi.org/10.3390/jcm9092899
Chicago/Turabian StyleShin, Dayeon, Kyung J. Lee, Temidayo Adeluwa, and Junguk Hur. 2020. "Machine Learning-Based Predictive Modeling of Postpartum Depression" Journal of Clinical Medicine 9, no. 9: 2899. https://doi.org/10.3390/jcm9092899
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