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Post-natal Head Circumference Prediction using ANN Regressor

In a developing country like India maternal education and socio-economic status of the family greatly affects the growth of the baby. The head circumference (HC) measure is a key factor in establishing the structural and functional growth of a child’s brain. Any deficiency which is accountable for improper development of a child’s brain which could be in terms of motor or physiological disorders requires attention. The prediction of HC measures are standardized by World Health Organization (WHO), for babies on the basis of their gender and age; but they can not be generalized for all location and socio-economic variations. Hence, presently, a prediction approach is proposed which can learn the relationship between the socio-economic status, age, and previous HC measure information to predict the baby HC measure 4 months ahead. The predicted HC measures could provide assistance to the medical practitioners and family a restorative intervention. This paper to predict baby HC measure has implemented an Artificial Neural Network (ANN) regressor. The dataset is collected from the Ganjam district, Odisha, India over varying socioeconomic status, maternal education, and gender variations. The incorporated ANN strongly correlates the factors socioeconomic status, gender, and past HC measure to predict the baby HC measure in 4 months time. The obtained results suggest that the ANN regressor could be a possible cost-effective solution for providing a child’s brain growth monitoring.