Leveraging Machine Learning Techniques to Assess the Importance of Maternal Health

Maternal health is of paramount importance in ensuring the well-being of newborn babies. In contemporary society, where individuals lead increasingly busy lives, understanding the consequences of maternity care remains crucial. The present state of newborns is linked to the maternal health during pregnancy. By using the ML techniques and DL algorithms analyze dataset related to maternal health indicators, healthcare utilization patterns, and socio-demographic factors. This learning aims to identify risk factors, and optimal interventions for enhancing maternal well-being and promoting positive neonatal outcomes. In this paper we used five supervised and Two DL algorithms. Our main aim is to insight to on the crucial factors which are Blood sugar, Blood pressure and age while we also consider age less than 19 years. We found that RF gives highest accuracy in all factors but in age less than 19 years GB gives highest accuracy Further we find classification reports of different models with accuracy score.