Prediction of Postpartum Depression Using Multilayer Perceptrons and Pruning - Tortajada, S.; García-Gómez, J. M.; Vicente, J.; Sanjuán, J.; de Frutos, R.; Martín-Santos, R.; García-Esteve, L.; Gornemann, I.; Gutiérrez-Zotes, A.; Canellas, F.; Carracedo, Á.; Gratacos, M.; Guillamat, R.; Baca-García, E.; Robles, M. (Methods of Information in Medicine, 2009)
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