Predictors of Body Mass Index among Pregnant Women in Nigeria: A Comparison of Ordinary Least Squares Regression and Quantile Regression Models Using Machine Learning Approach

Journal of Biometrics & Biostatistics

article

David Taiwo Ajayi¹  Segun Bello¹ 

[1] Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan

关键词: Body mass index;    Pregnant women;    Machine learning;    Ordinary least squares regression;    Quantile regression;    Cross-validation;   

DOI  :  10.4172/2155-6180.1000402

来源: Hilaris Publisher

【 摘 要 】

Poor nutrition during pregnancy is a major public health problem. Maternal under nutrition is a significant risk factorfor maternal morbidity, mortality, poor birth outcomes (e.g. low birth weight), and infant mortality. Maternal under nutritionis defined as having a body mass index (BMI) <18.5 kg/m2. Previous studies on maternal BMI utilized classical statisticalapproach, whose criteria for model assessment are goodness-of-fit test and residual examination. The aim of this studywas to identify predictors of BMI among pregnant women in Nigeria, and to compare the performance of ordinary leastsquares (OLS) regression and quantile regression using machine learning approach.This study utilized data from the 2013 Nigeria Demographic and Health Survey. A total of 3,049 pregnant women wereincluded in the study. Data were summarized using descriptive statistics. The assumption of normality of the outcomevariable (BMI) was tested using one-sample Kolmogorov-Smirnov test. Bivariate associations of BMI with independentvariables were assessed using robust (nonparametric) statistical techniques: Kendall’s tau correlation for continuouspredictors, Wilcoxon rank sum test for binary predictors and Kruskal-Wallis test for multinomial predictors. Predictors ofmaternal BMI were investigated using OLS and quantile regression analyses. Model assessment was made using 10-foldcross-validation. A two-tailed p-value <0.05 was considered statistically significant.The respondents had a mean age of 28.22 ± 6.30 years, and a mean BMI of 23.81 ± 4.18 kg/m2. Multivariate analysesidentified respondent’s age, duration of pregnancy, wealth class, and residence as predictors of maternal BMI. The crossvalidated mean squared error for the OLS regression model was lower than that for the quantile regression model.Respondent’s age, duration of pregnancy, wealth class, and residence were significantly associated with maternalBMI. OLS regression model fit the data more than the quantile regression model.

【 授权许可】

Unknown