This project was developed in response to the rising instances of child and maternal mortality in hospitals. It focuses on assessing the fetal health of children and maternal well-being. It examinies factors such as fetal heart rate (FHR), fetal movements, and uterine contractions. Furthermore, this project aims to compare three machine learning models specifically the Decision Tree, Random forest and KNN classifiers, to predict and evaluate Fetal Health Rate.
The dataset utilized for this project was otained from kaggle. it was derived from Cardiotocogram exams, which were meticulously categorized into three classes—Normal, Suspect, and Pathological—by expert obstetricians.