Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

• Main Authors: Molla, M. M. Imran, Jui, Julakha Jahan, Bari, Bifta Sama, Rashid, Mamunur, Hasan, Md Jahid
• Format: Conference or Workshop Item
• Language: English; English
• Published: Springer Singapore 2020
• Subjects: QA Mathematics; TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/27518/1/Cardiotocogram%20Data%20Classification%20using%20Random1.pdf; http://umpir.ump.edu.my/id/eprint/27518/2/Cardiotocogram%20Data%20Classification%20using%20Random.pdf; http://umpir.ump.edu.my/id/eprint/27518/; https://doi.org/10.1007/978-981-15-5281-6_25

The Cardiotocography is the most broadly utilized technique in obstetrics practice to monitor fetal health condition. The foremost motive of monitoring is to detect the fetal hypoxia at early stage. This modality is also widely used to record fetal heart rate and uterine activity. The exact analysis...