This paper is published in Volume-7, Issue-4, 2021
Area
Machine Learning
Author
Meghana S., Y. Sree Rushitha, Susan Syeda, Priyanka M., Dr. Shantakumar B. Patil
Org/Univ
Sai Vidya Institute of Technology, Bangalore, Karnataka, India
Pub. Date
15 July, 2021
Paper ID
V7I4-1342
Publisher
Keywords
Fetal birth weight estimation, Machine learning, linear regression, Random forest Regressor, XGB regression

Citationsacebook

IEEE
Meghana S., Y. Sree Rushitha, Susan Syeda, Priyanka M., Dr. Shantakumar B. Patil. Fetal birth weight estimation in high-risk pregnancies through Machine Learning Techniques, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Meghana S., Y. Sree Rushitha, Susan Syeda, Priyanka M., Dr. Shantakumar B. Patil (2021). Fetal birth weight estimation in high-risk pregnancies through Machine Learning Techniques. International Journal of Advance Research, Ideas and Innovations in Technology, 7(4) www.IJARIIT.com.

MLA
Meghana S., Y. Sree Rushitha, Susan Syeda, Priyanka M., Dr. Shantakumar B. Patil. "Fetal birth weight estimation in high-risk pregnancies through Machine Learning Techniques." International Journal of Advance Research, Ideas and Innovations in Technology 7.4 (2021). www.IJARIIT.com.

Abstract

Low birth weight of the fetus is considered one of the most critical problems in pregnancy care, which will affect the health of the newborn and in more severe cases will lead to its death. This situation is the reason for the high infant mortality rate throughout the world. In terms of health, artificial intelligence technologies, especially those based on machine learning (ML), can early predict problems related to the health of the fetus throughout pregnancy (even at birth). Therefore, the project proposes an analysis of several ML techniques that can predict whether the fetus will lose weight at birth in its gestational age. The importance of early diagnosis of problems related to fetal development depends on the possibility of increasing the number of days of pregnancy through timely intervention. This intervention will help to improve the weight of the fetus at birth, thus reducing neonatal morbidity and mortality.