Enhanced Optimal Feature Selection Techniques for Fetal Risk Prediction using Machine Learning Algorithms
- 1. Assistant Professor Senior at VIT University, Vellore, Tamilnadu, India.
- 2. Department of Computer Science Engineering VIT Vellore
Contributors
- 1. Publisher
Description
Cardiotocography (CTG) records fetal heart rate (FHR) and uterine contractions (UC) simultaneously. The CTG,*which is one of the*most common*diagnostic techniques used during pregnancy and before delivery to evaluate maternal and fetal well-being. Doctors can understand the state of the fetus by observing the*Cardiotocography trace patterns. There are several techniques for interpreting a typical cardiotocography data based on signal processing and computer programming. Only a few decades after cardiotocography has been implemented into clinical*practice, the predictive potential of these approaches remains controversial and still unreliable This paper presents MRMR feature selection algorithms with four classification for Fetal risk prediction using python.
Files
C6502029320.pdf
Files
(909.8 kB)
Name | Size | Download all |
---|---|---|
md5:708168c431bfdf3428e8d98ac2b6176b
|
909.8 kB | Preview Download |
Additional details
Related works
- Is cited by
- Journal article: 2249-8958 (ISSN)
Subjects
- ISSN
- 2249-8958
- Retrieval Number
- C6502029320 /2020©BEIESP