Cardiotocography is a powerful tool that is mainly used to determine how much oxygen the fetus is receiving, which is crucial for development. Classification models decision tree, support vector machine, gradient boost, k-nearest neighbors and logistic regression were applied to a multi-class dataset predict fetal health. 10-fold cross validation was used along with a grid search to perform hyper-parameter tuning. It was determined that gradient boost was the best performing model given this dataset, with a 0.99 value for area under the ROC curve.
