Reproductive intentions of the newlywed Bulgarian families--artificial neural network approach.
BACKGROUND
The aim of this study was to describe the reproductive intentions of newlywed Bulgarian families, build predictive models of the desired number of children by artificial neural networks (ANN) and compare these models' overall prediction accuracy with conventional statistical analyses.


METHODS
Data from a large cross-sectional study on family planning knowledge and habits of newlywed couples from the region of Plovdiv (Bulgaria) in the year 2000 were used. A subset of 17 variables was extracted from the database, in order to construct and train a back-propagation multilayer ANN. Desired number of children, separately for male and female spouses, was used as an output variable. The following statistical analyses were used: descriptive statistics, percentage distribution, nonparametric tests and logit regression analysis.


CONCLUSIONS
Factors that determine the opinion of male and female spouses on desired number of children have different relative importance in the phase space. The reproductive planning of the newly-married family can be better modeled and predicted in women than in men. The reproductive intentions of the modern newlywed Bulgarian family are decided on the basis of mutual agreement and understanding. There is no difference between desired number of children by both partners. The age at marriage of spouses is increasing with a tendency to reach the Western European patterns. Artificial neural networks are found to be a very useful tool in cross-sectional population studies for factor classification and outcome modeling, especially in cases where data are not entirely suitable for logistic regression analyses.
St Stefanov
Rumen
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Department of Social Medicine and Health Management, Medical University, Plovdiv, Bulgaria. u1315@yahoo.com
D002031:Bulgaria; D003430:Cross-Sectional Studies; D005193:Family Planning Services; D005260:Female; D006801:Humans; D008297:Male; D008393:Marriage; D009415:Nerve Net; D012044:Regression Analysis; D011795:Surveys and Questionnaires