Paper Title
Fetal birth weight estimation in High-risk pregnancies
Article Identifiers
Authors
Chandana C , Jayanth Kumar S , Pramod Narayan Pattar , Prathyusha Sajja , Shashank B L
Keywords
Random Forest (RF), Ultrasound, customized growth charts, multiple regression analysis, fetal surveillance, delivery management.
Abstract
Fetal birth weight estimation is an essential part of obstetric care, particularly in high-risk pregnancies where fetal growth may be compromised. The accuracy of fetal birth weight estimation guides decisions on the timing and mode of delivery, potentially improving outcomes for the mother and baby. There are different methods used to estimate fetal weight, including clinical assessment, ultrasound-based formulas, and customized growth charts. Factors that can affect fetal growth, such as maternal conditions and fetal factors, are also examined. Ultrasound-based formulas are more accurate and reliable in fetal weight estimation. They involve the use of ultrasound measurements of fetal biometry, such as head circumference, abdominal circumference, and femur length, to estimate fetal weight. These formulas are based on mathematical models that use multiple regression analysis to predict fetal weight. In recent years, machine learning techniques such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and Decision Trees (DT) have been used in fetal birth weight estimation models. These techniques use a combination of ultrasound measurements and maternal variables to predict fetal weight. They have shown promising results in improving the accuracy of fetal weight estimation in high-risk pregnancies. Ongoing fetal surveillance is vital in high-risk pregnancies to detect growth abnormalities and facilitate appropriate management. A system like the one proposed here provides valuable insights for clinicians managing high-risk pregnancies, enabling them to make informed decisions regarding fetal weight estimation and delivery management.
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How To Cite
"Fetal birth weight estimation in High-risk pregnancies", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 5, page no.b52-b65, May-2023, Available :https://ijnrd.org/papers/IJNRD2305110.pdf
Issue
Volume 8 Issue 5, May-2023
Pages : b52-b65
Other Publication Details
Paper Reg. ID: IJNRD_193541
Published Paper Id: IJNRD2305110
Downloads: 000121109
Research Area: Computer Science & TechnologyÂ
Country: Bengaluru, Karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2305110.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305110
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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