Fetal Health Classification Using Various Machine Learning Algorithm

The aim of this study is to compare various supervised machine learning algorithms for predicting the fetal health. In this study, the researcher utilizes numerical data to classify the health of the fetal using various supervised machine learning algorithms. By comparing the performance of different algorithms, such as KNearest Neighbors, Random Forest, Decision Tree and Logistic Regression, the best algorithm for classifying the fetal health. The study focuses on using numerical data such as baseline value, accelerations, fetal movement, uterine contraction, decelerations, short term variability to train and test the algorithms. The accuracy of each algorithm is measured, and the results are compared to identify the most effective algorithm for classification of fetal health. This study has the potential to provide valuable insights into the use of machine learning for prediction of fetal health and can assist healthcare professionals in making early and accurate diagnoses.