Fetal birth weight estimation in High-risk pregnancies

Registration ID: IJNRD_193541

Published ID: IJNRD2305110

DOI: Click Here to Get

Chandana C , Jayanth Kumar S , Pramod Narayan Pattar , Prathyusha Sajja , Shashank B L

Random Forest (RF), Ultrasound, customized growth charts, multiple regression analysis, fetal surveillance, delivery management.

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.

Published Paper   E-Certificate

Chandana C , Jayanth Kumar S , Pramod Narayan Pattar , Prathyusha Sajja , & Shashank B L (May-2023). Fetal birth weight estimation in High-risk pregnancies. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), b52-b65. https://ijnrd.org/papers/IJNRD2305110.pdf

Volume 8 Issue 5, May-2023

Pages : b52-b65

Paper Reg. ID: IJNRD_193541

Published Paper Id: IJNRD2305110

Downloads: 000121977

Research Area: Computer Science & Technology 

Country: Bengaluru, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305110.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305110

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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