IJBTT

Prediction of Birth Weight Using Machine Learning Models

Prediction of Birth Weight Using Machine Learning Models
	© 2025 by IJETT Journal
	Volume-73 Issue-4
	Year of Publication : 2025
	Author : Keerthana P, Sasidhar Babu Suvanam
	DOI : 10.14445/22315381/IJETT-V73I4P136

How to Cite?
Keerthana P, Sasidhar Babu Suvanam, "Prediction of Birth Weight Using Machine Learning Models," International Journal of Engineering Trends and Technology, vol. 73, no. 4, pp.440-452, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I4P136

Abstract
The study focuses on the fetal birthweight using machine learning (ML) based prediction models. The software used for this research is Python, which is used to design and implement the birthweight forecasting model. The model developed uses six different regression models, using Linear, Ridge, ElasticNet, SVM, XGB and Ensemble (XGB+GB) models, respectively. The performances of each regression model are evaluated using R2 estimation. The results show that the ensemble model achieved a higher accuracy level of 90% (.9039392149835611), whereas the other five models obtained less than 80%. The accuracy achieved by the elastic-net model is 34%, ridge and linear models are 39%, the SVM mode gained 42.9%, and the XGB model achieved 77.7%. From the findings, it is proven that the XGB+GB ensemble model obtained higher accuracy with better performance. Thus, from the results, it can be concluded that the ensemble model is more efficient in accurately predicting the birth weight of fetuses than other traditional models.

Keywords
Birthweight, Ensemble model, Fetal birthweight prediction, Fetuses birthweight prediction, Regression models, R squared estimation.

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