Abstract:
Malnutrition and stunting which comes from it can be put in the list of the top problems
of any developing countries. A country like Bangladesh is no different. The principal
contribution of this study is to predict the Stunting status of the children and also to
search for the related affecting factors which affect the nutrition status. In this research,
data has been collected from Bangladesh Demographic Health Survey (BDHS)’2017-18.
Nutrition status correlated with the child’s age, mother’s education, father’s education,
father’s employment, family wealth index, currently breastfeeding, place of residence and
division. The differential impact of some sectors like demographical and
socioeconomical, environment plus health-affecting determinants on the nutritional rank
within the population of under-five children in Bangladesh has been taken into account.
To measure the child nutritional condition of under-five children among various methods,
this Z-score method is one which we have used in this paper to find the statistics of
malnutrition in Bangladesh. Methods provided by WHO (World Health Organization)
was followed to find out the necessary outcomes. Here, Chi-square statistics algorithm
has been applied to find out the factors which are most responsible for stunting by
ranking features and then applied machine learning algorithms for a prediction model.
Two algorithms have been used here and based on the performance results and parameter
we find Logistic Regression gave most accuracy out of them all. It’s found that based on
all features the accuracy is 77.00% and based on top 5 features the accuracy is 63.00%
and based on top 7 features the accuracy is 77.00% which is better than the outcomes of
Random Forest Algorithm. The study is suggested to focus upon the factors which are
responsible for malnutrition and it would ensure healthy nation
