Prediction of postpartum depression using multilayer perceptrons and pruning.
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.


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.
Tortajada
Salvador
S
IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain. vesaltor@upvnet.upv.es
García-Gomez
Juan M
JM
Vicente
Javier
J
Sanjuán
Julio
J
de Frutos
Rosa
R
Martín-Santos
Rocío
R
García-Esteve
Luisa
L
Gornemann
Isolde
I
Gutiérrez-Zotes
Alfonso
A
Canellas
Francesca
F
Carracedo
Angel
A
Gratacos
Monica
M
Guillamat
Roser
R
Baca-García
Enrique
E
Robles
Montserrat
M
D000328:Adult; D000465:Algorithms; D015331:Cohort Studies; D019052:Depression, Postpartum / Q000175:diagnosis*; D005260:Female; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D009415:Nerve Net; D011446:Prospective Studies; D013030:Spain