MEGHA CHATURVEDI, SHIKHA AGRAWAL AND SANJAY SILAKARI
Department of Computer Science and Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal-462 033
(Madhya Pradesh), India
*(e-mail : chaturvedimegha09@gmail.com; Mobile : 91 83494 08181)
(Received : November 5, 2021; Accepted : January 17, 2022)
ABSTRACT
Early detection of any abnormalities can give further insights into the pregnancy and will provide more
time to parents and doctors to prepare for these unnatural circumstances. Cardiotocography (CTG) is a
technique used for monitoring fetal heart rate. It is widely used to ensure fetal well-being during
pregnancies at high risk. Use of machine-learning techniques automated this task and reduced the
chances of diagnostic errors. Deep learning also has powerful algorithms for learning complicated
characteristics and higher-level semantics. The principal objective of this paper was to dissect the
boundaries of different classification algorithms and contrast their prescient exactnesses to discover
the best classifier for ordering fetal well-being.
Key words : : Cardiotocography (CTG), machine learning, classification algorithms, perinatal mortality