MODELLING NUTRITIONAL STATUS OF WOMEN OF REPRODUCTIVE AGE IN NIGERIA: A MULTINOMIAL LOGISTIC REGRESSION MODEL WITH NEURAL NETWORK COMPONENT APPROACH

Abstract

This study focuses on modelling the nutritional status of women of reproductive age in Nigeria using a hybrid approach combining multinomial logistic regression model with a neural network component. A secondary data extracted from Multiple Indicators Cluster Survey (MICS 6: 2021-2022) report and Global Nutrition Publication (GNP, 2020) were the sources of data for the study. The survey was conducted by National Bureau of Statistics in collaboration with other international agencies while data analysis was carried out with the aid of STATA Software version 16.0. Study results show that all coefficients of predictor variables greater than 3.84 of chi-square distribution are statistically significant in the modified model with respect to nutritional status of WRA. The study also revealed that all predicted variables with of less than 0.05 significance level are significantly associated with outcome variables (underweight, overweight and obesity). By integrating multinomial logistic regression for interpretability with neural network components for enhanced predictive accuracy, this study provides a robust modelling framework. The findings could inform targeted interventions and policy decisions to improve the nutritional status of women of reproductive age in Nigeria, ultimately contributing to better maternal and child health outcomes. It was recommended that establishment of a comprehensive database on women’s health and nutrition in Nigeria, incorporating socio-demographic, economic and health-related factors after proper data collection is ensured.