EARLY PREDICTION OF LOWBIRTH WEIGHT CASES USING ML

Abstract

This work aims to predict, from a variety of user inputs, whether a baby will be born healthy or underweight. Taking into account characteristics including parental health, ethnicity, educational background, and region—all of which have an impact on healthcare accessibility and environmental factors—the study acknowledges the significance of birth weight in relation to gestational age. Through the examination of extensive datasets containing these lifestyle and demographic characteristics, health care providers can improve prenatal care and interventions, concentrating more carefully on populations that are at risk. With the help of user-supplied data, this prediction tool provides a probabilistic estimate of birth weight outcomes, giving parents and medical professionals peace of mind and assistance. Keyword: Low Birth weight (LBW), Smart health informatics, Machine Learning (ML).