Impact of Education and Socio-Economic Status on Post-Natal Body Weight using Machine Learning Approach

Abstract

Background: The maternal education and socio-economic status of the mother greatly affect the growth of
the baby. Nutritional deficiency is the major cause of motor and physiological disorders in children. Hence,
predicting body weight could be used to monitor the growth of the babies. Although the prediction of
weights is standardized by World Health Organization (WHO), for babies based on their gender and age;
generalizing it for all locations and socio-economic variations is not possible
Method: Hence, in this paper, an ANN-based predictive approach to establish the relationship between
the factors like socioeconomic status, age, and prior weights to predict the baby’s weight using a locally
acquired dataset. This could be helpful for the health workers to properly diagnose the disease a time ahead.
Conclusion: The proposed work suggests predicting the baby’s growth rate using Machine Learning (ML)
techniques is both an efficient and feasible approach.