Prediction of Postpartum Depression Using Multilayer Perceptrons and Pruning

Summary

Objective: The main goal of this paper is to obtain a classification model based on feed-forward multilayer perceptrons in order to improve postpartum depression prediction during the 32 weeks after childbirth with a high sensitivity and specificity and to develop a tool to be integrated in a decision support system for clinicians.

Materials and Methods: Multilayer perceptrons were trained on data from 1397 women who had just given birth, from seven Spanish general hospitals, including clinical, environmental and genetic variables. A prospective cohort study was made just after delivery, at 8 weeks and at 32 weeks after delivery. The models were evaluated with the geometric mean of accuracies using a hold-out strategy.

Results: Multilayer perceptrons showed good performance (high sensitivity and specificity) as predictive models for postpartum depression.

Conclusions: The use of these models in a decision support system can be clinically evaluated in future work. The analysis of the models by pruning leads to a qualitative interpretation of the influence of each variable in the interest of clinical protocols.

Keywords

Multilayer perceptron - neural network - pruning - postpartum depression

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