title: Prediction of postpartum depression using multilayer perceptrons and pruning.
abstract: 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.
authors.affiliation: 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
authors.forename: Salvador
authors.initials: S
authors.lastname: Tortajada
authors.forename: Juan M
authors.initials: JM
authors.lastname: García-Gomez
authors.forename: Javier
authors.initials: J
authors.lastname: Vicente
authors.forename: Julio
authors.initials: J
authors.lastname: Sanjuán
authors.forename: Rosa
authors.initials: R
authors.lastname: de Frutos
authors.forename: Rocío
authors.initials: R
authors.lastname: Martín-Santos
authors.forename: Luisa
authors.initials: L
authors.lastname: García-Esteve
authors.forename: Isolde
authors.initials: I
authors.lastname: Gornemann
authors.forename: Alfonso
authors.initials: A
authors.lastname: Gutiérrez-Zotes
authors.forename: Francesca
authors.initials: F
authors.lastname: Canellas
authors.forename: Angel
authors.initials: A
authors.lastname: Carracedo
authors.forename: Monica
authors.initials: M
authors.lastname: Gratacos
authors.forename: Roser
authors.initials: R
authors.lastname: Guillamat
authors.forename: Enrique
authors.initials: E
authors.lastname: Baca-García
authors.forename: Montserrat
authors.initials: M
authors.lastname: Robles
mesh_terms: 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