Machine Learning Approaches to Predict Health Status of Fetus based on Maternity Data: A Review

Afetus is an incipient infant that is still an embryo in the womb. During this gestation period, the infant develops and expands, making routine tests extremely necessary. As we all know, gestation period is nine months, and there can be a variety of factors that might result in the newborn's impairment or death, which is a very serious crime that should be avoided. Another key tool for diagnosing fetal health is CTG (Cardiotocography) which is commonly used to check the heartbeat and cervical mucus which is why the information produced is utilized by the physician to evaluate fitness and well-being. But there will be lot of errors while evaluating it manually which is why doctors are not reliable in analysing data which is why different Machine Learning methods were available that could analyse data and predict an infant’s condition based on it. The paper's major goal is to review the various accuracy score of the prediction using several categorization models and to differentiate models that work finest.