Enhanced Optimal Feature Selection Techniques for Fetal Risk Prediction using Machine Learning Algorithms
J. Jayashree1, Harsha T2, Anil Kumar C3, J. Vijayashree4
1J. Jayashree*, Assistant Professor Senior at VIT University, Vellore, (Tamil Nadu), India.
2Harsha T, Assistant Professor Senior at VIT University, Vellore, (Tamil Nadu), India.
3Anil Kumar, Department of Computer Science Engineering VIT Vellore, (Tamil Nadu), India.
4J.Vijayashree, Assistant Professor Senior at VIT University, Vellore, (Tamil Nadu), India.
Manuscript received on January 22, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 4364-4370 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6502029320/2020©BEIESP | DOI: 10.35940/ijeat.C6502.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Fetal heart rate, Cardiotocography, Uterine contractions, Machine learning, MRMR, Python.