Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

Authors

  • Ebrahim Hajizadeh Department of Biostatistics, Faculty of Medical Sciences, TarbiatModares University, Tehran, Iran.
  • Farzad Ebrahimzadeh Department of Statistics and Epidemiology, Faculty of Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, & PhD Candidate in Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, TarbiatModares University, Tehran, Iran.
  • Katayoon Bakhteyar Department of Public Health, Faculty of Health and Nutrition, Lorestan University of Medical Sciences, Khorramabad, Iran.
  • Mohammad Almasian Department of the English Language, Faculty of Medicine, Lorestan University of Medical Sciences, Khorramabad, Iran.
  • Nasim Vahabi Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Abstract:

  Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population.   Methods : In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used.   Results : The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively.   Conclusion : Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

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Journal title

volume 29  issue 1

pages  828- 832

publication date 2015-01

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