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

Abstract

Poor nutrition during pregnancy is a major public health problem. Maternal under nutrition is a significant risk factor for maternal morbidity, mortality, poor birth outcomes (e.g. low birth weight), and infant mortality. Maternal under nutrition is defined as having a body mass index (BMI) <18.5 kg/m2. Previous studies on maternal BMI utilized classical statistical approach, whose criteria for model assessment are goodness-of-fit test and residual examination. The aim of this study was to identify predictors of BMI among pregnant women in Nigeria, and to compare the performance of ordinary least squares (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 were included in the study. Data were summarized using descriptive statistics. The assumption of normality of the outcome variable (BMI) was tested using one-sample Kolmogorov-Smirnov test. Bivariate associations of BMI with independent variables were assessed using robust (nonparametric) statistical techniques: Kendall’s tau correlation for continuous predictors, Wilcoxon rank sum test for binary predictors and Kruskal-Wallis test for multinomial predictors. Predictors of maternal BMI were investigated using OLS and quantile regression analyses. Model assessment was made using 10-fold cross-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 analyses identified 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 maternal BMI. OLS regression model fit the data more than the quantile regression model. Citation: Ajayi DT, Bello S (2018) 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. J Biom Biostat 9: 402. doi: 10.4172/2155-6180.1000402