Predictive Modeling and Analysis of Fetal Growth using Linear Regression and Random Forest
DOI:
https://doi.org/10.48001/978-81-966500-2-5-10Keywords:
Aritifical Inteeligence, Fetal Weight, Headlock Formula, RegressorAbstract
Low fetal birth weight is a critical concern in pregnancy care, significantly affecting neonatal health and contributing to high infant mortality rates globally. Low birth weight is associated with numerous health complications, such as respiratory distress, infections, and long-term developmental challenges. Early diagnosis of fetal growth issues is crucial, as it enables timely medical interventions to prolong the gestation period, allowing more time for fetal development and increasing the likelihood of a healthier birth weight. This project aims to develop a predictive model to estimate fetal birth weight early in pregnancy, categorizing the results as low (< 2.5 kg), normal (2.5–4.5 kg), or abnormal (> 4.5 kg).
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