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

نویسندگان

  • Farzad Ebrahimzadeh
  • Ebrahim Hajizadeh
  • Nasim Vahabi
  • Mohammad Almasian
  • Katayoon Bakhteyar
چکیده

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.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

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

  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 ...

متن کامل

Comparison of Gestational Diabetes Prediction Between Logistic Regression, Discriminant Analysis, Decision Tree and Artificial Neural Network Models

Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these m...

متن کامل

Variable Selection Method Affects SVM Approach in Bankruptcy Prediction

This paper examined bankruptcy predictive accuracy of five statistics models-discriminant analysis logistic regression, probit regression, neural networks, support vector machine (SVM), and genetic-based SVM (GA-SVM) that influenced by variable selection. Empirical results indicate that the SVM-based models are very promising models for predicting financial failure, in terms of both best predic...

متن کامل

Factors Affecting Unplanned Pregnancy in Semnan Province, Iran

Background & aim: Despite the success of family planning programs in Iran in the recent decades, considerable proportions of pregnancies are still unintended and can be a cause of poor mental and physical health of the mother and child. The aim of this study was to investigate some important factors affecting uplanned pregnancies among married women in Semnan province, one of the developed prov...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره 29  شماره 

صفحات  -

تاریخ انتشار 2015