UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 9 Issue 6
June-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2206849


Registration ID:
405002

Page Number

i397-i412

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Title

FETAL HEALTH CLASSIFICATION AND PREDICTION USING MACHINE LEARNING

Authors

Abstract

An estimated 2.8 million pregnant women and newborns die every year, or 1 every 11 seconds, mostly of preventable causes, according to new mortality estimates released by UNICEF, the World Health Organization (WHO), the United Nations Population Division, UNFPA and the World Bank Group. “Around the world, birth is a joyous occasion. Yet, every 11 seconds, a birth is a family tragedy,” said Henrietta Fore, UNICEF Executive Director. About 700 women die each year in the United States as a result of pregnancy or delivery complications. In the current scenario , It is very hard to go out of the house even for a medical check up due to covid, traffic in metropolitan cities , and tough working hours in the office. Then imagine the situation of a pregnant woman, Since she needs to go out for her regular check up. She will be traveling from place to place as part of her check up like laboratories, Hospitals etc there by making her expenses high and also by performing this amount of work she will be very tired which is not good for her as well as for her fetus. A fetus is an unborn child who is still in the embryonic stage till it is born into the world. Trimester is the name given to each three-month period throughout the pregnancy process. The fetus grows and develops throughout this time, and regular checkups are essential. As we all know, a pregnancy lasts 9 months, and during that time, a variety of factors can cause disability or death in the newborn, which is a very serious situation that must be avoided. Doing a CTG (Continuous Cardiotocography), which is commonly used to evaluate the heart beat and the health of the fetus in the pregnancy, is one of the key tools for analyzing the health of the fetus in the womb. Although there are so many ways of knowing health. Each way is dependent on only one department, that is the laboratory. Based on the results the woman gets from the laboratory we will predict the fetal health by using the Random Forest Classifier algorithm where we will pass the results entered by the user and predict the health of the fetus. By doing these predictions, we can reduce the expenses spent for the check-ups , reduce waiting time for pregnant women in hospitals and reduce the stress and tiredness. This is our 3R’S and MSP’s.

Key Words

Machine Learning, Linear Regression, Support Vector Machine, Prediction analysis.

Cite This Article

"FETAL HEALTH CLASSIFICATION AND PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.i397-i412, June-2022, Available :http://www.jetir.org/papers/JETIR2206849.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"FETAL HEALTH CLASSIFICATION AND PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppi397-i412, June-2022, Available at : http://www.jetir.org/papers/JETIR2206849.pdf

Publication Details

Published Paper ID: JETIR2206849
Registration ID: 405002
Published In: Volume 9 | Issue 6 | Year June-2022
DOI (Digital Object Identifier):
Page No: i397-i412
Country: tumkur, Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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