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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Fetal birth weight estimation in High-risk pregnancies |
| Authors Name: | Chandana C , Jayanth Kumar S , Pramod Narayan Pattar , Prathyusha Sajja , Shashank B L |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_193541
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| Published Paper Id: | IJNRD2305110 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| 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. |
| Keywords: | Random Forest (RF), Ultrasound, customized growth charts, multiple regression analysis, fetal surveillance, delivery management. |
| Cite Article: | "Fetal birth weight estimation in High-risk pregnancies", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.b52-b65, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305110.pdf |
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| 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 |
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Published Paper ID:IJNRD2305110 Registration ID: 193541 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: b52-b65 Country: Bengaluru, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305110 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305110 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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