NON - PARAMETRIC MULTIPLE INPUTS PREDICTION MODEL FOR QUANTITY OF INFANT-FORMULA DEMANDED FOR CHILDREN AGED 0 - 24 MONTHS IN ILORIN-SOUTH, NIGERIA
Abstract
Infant-formula is recommended by the World Health Organisation as the safest form of complementary food to human breast milk. Breastfeeding duration and other imperative input-factors that govern quantity of infant-formula demanded (a modulus variable) is difficult to model and predict with high precision. However, infants based on their conventional rights have to be given good nutrition. This study is necessitated by the challenging process of complex modulus prediction of quantity of infant-formula demanded to complement human breast milk, which may be attributed to differences in maternal characteristics at various maternal geographical settlement areas. Owning to complex nature of the differences, we propose a data-driven approach for handling larger-scale scenarios which may be limited in the conventional parametric modelling methods. This study aims at investigating the association of maternal characteristics with quantity of infant -formula demanded among breastfeeding mothers who have children aged 0 – 24 months in Ilorin-South Local Government Area, Nigeria. Primary data were collected from 744 breastfeeding mothers. We adopted appropriate non-parametric tests to determine the impact of different independent groups of maternal covariate factors considered on quantity of infant -formula demanded. Multi-associations were assessed and negative binomial model was formulated to predict estimated quantity of infant-formula demanded for various breastfeeding durations. The results show that increase in breastfeeding duration leads to increase in quantity of infant-formula demanded. In future intervention studies, factors that have significant effect on demand for infant-formula might be considered for proper monitoring of infant feeding.
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