Automatic detection of fetal health status from cardiotocography data using machine learning algorithms

A method for the automatic determination of fetus health status using Cardiotocography (CTG) and computer-based machine learning algorithms was developed. Five computation friendly were used to create multiclass classification models predict from secondary CTG dataset containing normal, suspected pathologic data available at University California Irvine Machine Learning Repository. Furthermore, a comparative analysis among built executed. According analysis, best model automatically detect fetal extreme gradient boosting algorithm-based with an accuracy 96.7% F1-Score 0.963 in class. This finding thus has potential diagnose heart conditions unsupervised, more efficiently effectively. J. Bangladesh Acad. Sci. 45(2); 155-167: December 2021