Predicting Unwanted Pregnancies among Multiparous Mothers in Khorramabad, Iran
نویسندگان
1 Department of Biostatistics, Faculty of Medicine, Tarbiat Modarres University, Tehran, IR Iran
2 Department of Statistics, Faculty of Sciences, Amirkabir University of Technology, Tehran, IR Iran
3 Instructor of English Language Teaching, Department of English language, Faculty of Medicine, Lorestan University of Medical Sciences, Khorramabad, IR Iran
4 Department of Biostatistics, Faculty of Medicine, Tarbiat Modarres University, Tehran, IR Iran
5 Instructor of Midwifery , Department of Public Health, Faculty of Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, IR Iran
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چکیده
Background: Unwantedpregnancy is the kind of pregnancy which is undesirable for at least one of the parents, andis accompaniedby unfavorable consequences for the family and society.Objectives: In this study, three classification models have been used to predict the occurrence of unwanted pregnancies in theurban population in Khorramabad, Iran, and the performance of these models was compared.Methods: In this cross-sectional study, 467 multiparousmothersreferred to the health centers of Khorramabadin 2012wereselectedusing a combination of cluster and stratified sampling, and the relevant variables were measured. The logistic regression, decisiontree, and a neural network were implemented using SPSS version 21 and MATLAB version R2013a. To compare these models, theindices of sensitivity and specificity, the area under the ROC curve, and the correct percentage of the predictions were used.Results: Overall, the prevalence of unwanted pregnancies was 32.3%. The performance of the models based on the area under theROC curve as the indicator was as follows: artificial neural networks (0.741), decision tree (0.731), and logistic regression (0.712).The highest sensitivity level belonged to the decision tree (73.5%), and the highest specificity level belonged to the artificial neuralnetwork (62.3%).Conclusions: Given the high prevalence of unwanted pregnancies in Khorramabad, Iran, it is necessary to revise and improve thefamily planning projects. In selecting the best classification method, if the researcher is interested in the better interpretability ofthe results, the use of the decision tree and logistic regression is recommended; however, if the researcher is interested in a higherprediction power of the model, the neural network is recommended.
