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Original Article
Risk Aware Birth Weight Prediction System
Thota Hyma1
Shaik Arshiya Bano2
Sattiraju Sita Rama Srikanth3
Vallabhaneni Snigdha4
Shaik Arshad Ahmad5
L.N.B. Jyotsna6
1 2 3 4 5 Department of CSE, Dhanekula Institute of Engineering and Technology, Andhra Pradesh, India. 6 Assistant Professor, Department of CSE, Dhanekula Institute of Engineering and Technology, Andhra Pradesh, India.
Published Online: March-April 2026
Pages: 262-273
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702032
References
1. M. Feng, L. Wan, Z. Li, L. Qing, and X. Qi, “Fetal Weight Estimation Via Ultrasound Using Machine Learning,” IEEE Access, DOI:10.1109/ACCESS.2019.2925803, 2019.
2. M. S. Alom, A. S. M. S. Ahamed, and O. Haque, “A Comprehensive Approach to Fetal Health Prediction Using Machine Learning and Ensemble Models,” in Proc. 27th Int. Conf. Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh, Dec. 2024, pp.2345– 2350, doi:10.1109/ICCIT64611.2024.11022051.
3. M. F. Ukrit, L. R. Aluru, and V. Chandana, “Fetal Health Classification and Visceral Fat Level Prediction using Gradient Boosting and Deep Learning Techniques,” in Proc. 2nd Int. Conf. Networking and Communications (ICNWC), 2024, doi:10.1109/ICNWC60771.2024.10537531.
4. S. A. MJ, S. SR, C. Ramadevi, and T. V. Narmadha, “Effective Feature Technique for Heart Disease [FETUS] Prediction in Machine Learning,” in Proc. Int. Conf. Frontier Technologies and Solutions (ICFTS), 2025, doi:10.1109/ICFTS62006.2025.11031565.
5. N. Rahmayanti, H. Pradani, M. Pahlawan, and R. Vinarti, “Comparison of machine learning algorithms to classify fetal health using cardiotocogram data,” Procedia Computer Science, vol.197, pp.162– 171, 2022.
6. H. Varshney and A. Singh, “A Machine Learning-Based Prediction Model for Fetal Health Assessment,” in Frontiers of ICT in Healthcare: Proc. EAIT 2022, Springer, 2023, pp.239–250.
7. A. Akbulut, E. Ertugrul, and V. Topcu, “Fetal health status prediction based on maternal clinical history using machine learning techniques,” Computer Methods and Programs in Biomedicine, vol.163, pp.87–100, 2018.
8. Y. Zhang and Z. Zhao, “Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost,” in Proc. 10th Int. Congress Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017, pp.1–6.
9. Y. Gong et al., “Fetal congenital heart disease echocardiogram screening based on DGACNN: Adversarial One-class classification combined with Video transfer learning,” IEEE Trans. Medical Imaging, vol.39, no.4, pp.1206–1222, 2020.
10. P. A. Warrick, E. F. Hamilton, D. Precup, and R. E. Kearney, “Classification of Normal and Hypoxic Fetuses From Systems Modeling of Intrapartum Cardiotocography,” IEEE Trans. Biomedical Engineering, vol.57, no.4, pp.771–779, Apr. 2010.
11. G. Georgoulas, D. Stylios, and P. Groumpos, “Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines,” IEEE Trans. Biomedical Engineering, vol.53, no.5, pp.875–884, May 2006.
12. J. Spilka et al., “Using nonlinear features for fetal heart rate classification,” Biomedical Signal Processing and Control, vol.7, no.4, pp.350– 357, Jul. 2012.
13. F. P. Hadlock, R. B. Harrist, R. S. Sharman, R. L. Deter, and S. K. Park, “Estimation of fetal weight with the use of head, body, and femur measurements—a prospective study,” Am. J. Obstetrics and Gynecology, vol.151, no.3, pp.333–337, 1985.
14. M. J. Shepard, V. A. Richards, R. L. Berkowitz, S. L. Warsof, and J. C. Hobbins, “An evaluation of two equations for predicting fetal weight by ultrasound,” Am. J. Obstetrics and Gynecology, vol.142, no.1, pp.47– 54, 1982.
15. N. J. Dudley, “A systematic review of the ultrasound estimation of fetal weight,” Ultrasound in Obstetrics and Gynecology, vol.25, no.1, pp.80– 89, 2005.
16. L. Breiman, “Random Forests,” Machine Learning, vol.45, no.1, pp.5– 32, 2001.
17. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol.29, no.5, pp.1189–1232, 2001.
18. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” J. Artificial Intelligence Research, vol.16, pp.321–357, 2002.
19. Niculescu-Mizil and R. Caruana, “Predicting good probabilities with supervised learning,” in Proc. 22nd Int. Conf. Machine Learning (ICML), pp.625–632, 2005.
20. L. I. Kuncheva and C. J. Whitaker, “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,” Machine Learning, vol.51, no.2, pp.181–207, 2003.
21. Q. McNemar, “Note on the sampling error of the difference between correlated proportions or percentages,” Psychometrika, vol.12, no.2, pp.153–157, 1947.
22. G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol.18, no.7, pp.1527–1554, 2006.
23. H. Sahin and A. Subasi, “Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques,” Applied Soft Computing, vol.33, pp.231–238, Aug. 2015.
24. L. Lin, I. A. Dekkers, Q. Tao, and H. J. Lamb, “Novel artificial neural network and linear regression based equation for estimating visceral adipose tissue volume,” Clinical Nutrition, vol.39, no.10, pp.3182– 3188, 2020.
25. S. Badshah et al., “Risk factors for low birth weight in the public hospitals at Peshawar, NWFP-Pakistan,” BMC Public Health, vol.8, p.197, 2008.
26. H. G. Mengesha, A. D. Wuneh, B. Weldearegawi, and S. D. Misgina, “Low birth weight and macrosomia in Tigray, Northern Ethiopia: who are the mothers at risk?,” BMC Pediatrics, vol.17, no.1, p.144, 2017.
27. T. Henriksen, “The macrosomic fetus: a challenge in current obstetrics,” Acta Obstetricia et Gynecologica Scandinavica, vol.87, no.2, pp.134– 145, 2008.
28. M. Bernstein and P. M. Catalano, “Influence of fetal fat on the ultrasound estimation of fetal weight in diabetic mothers,” Obstetrics and Gynecology, vol.79, no.4, pp.561–563, 1992.
2. M. S. Alom, A. S. M. S. Ahamed, and O. Haque, “A Comprehensive Approach to Fetal Health Prediction Using Machine Learning and Ensemble Models,” in Proc. 27th Int. Conf. Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh, Dec. 2024, pp.2345– 2350, doi:10.1109/ICCIT64611.2024.11022051.
3. M. F. Ukrit, L. R. Aluru, and V. Chandana, “Fetal Health Classification and Visceral Fat Level Prediction using Gradient Boosting and Deep Learning Techniques,” in Proc. 2nd Int. Conf. Networking and Communications (ICNWC), 2024, doi:10.1109/ICNWC60771.2024.10537531.
4. S. A. MJ, S. SR, C. Ramadevi, and T. V. Narmadha, “Effective Feature Technique for Heart Disease [FETUS] Prediction in Machine Learning,” in Proc. Int. Conf. Frontier Technologies and Solutions (ICFTS), 2025, doi:10.1109/ICFTS62006.2025.11031565.
5. N. Rahmayanti, H. Pradani, M. Pahlawan, and R. Vinarti, “Comparison of machine learning algorithms to classify fetal health using cardiotocogram data,” Procedia Computer Science, vol.197, pp.162– 171, 2022.
6. H. Varshney and A. Singh, “A Machine Learning-Based Prediction Model for Fetal Health Assessment,” in Frontiers of ICT in Healthcare: Proc. EAIT 2022, Springer, 2023, pp.239–250.
7. A. Akbulut, E. Ertugrul, and V. Topcu, “Fetal health status prediction based on maternal clinical history using machine learning techniques,” Computer Methods and Programs in Biomedicine, vol.163, pp.87–100, 2018.
8. Y. Zhang and Z. Zhao, “Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost,” in Proc. 10th Int. Congress Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017, pp.1–6.
9. Y. Gong et al., “Fetal congenital heart disease echocardiogram screening based on DGACNN: Adversarial One-class classification combined with Video transfer learning,” IEEE Trans. Medical Imaging, vol.39, no.4, pp.1206–1222, 2020.
10. P. A. Warrick, E. F. Hamilton, D. Precup, and R. E. Kearney, “Classification of Normal and Hypoxic Fetuses From Systems Modeling of Intrapartum Cardiotocography,” IEEE Trans. Biomedical Engineering, vol.57, no.4, pp.771–779, Apr. 2010.
11. G. Georgoulas, D. Stylios, and P. Groumpos, “Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines,” IEEE Trans. Biomedical Engineering, vol.53, no.5, pp.875–884, May 2006.
12. J. Spilka et al., “Using nonlinear features for fetal heart rate classification,” Biomedical Signal Processing and Control, vol.7, no.4, pp.350– 357, Jul. 2012.
13. F. P. Hadlock, R. B. Harrist, R. S. Sharman, R. L. Deter, and S. K. Park, “Estimation of fetal weight with the use of head, body, and femur measurements—a prospective study,” Am. J. Obstetrics and Gynecology, vol.151, no.3, pp.333–337, 1985.
14. M. J. Shepard, V. A. Richards, R. L. Berkowitz, S. L. Warsof, and J. C. Hobbins, “An evaluation of two equations for predicting fetal weight by ultrasound,” Am. J. Obstetrics and Gynecology, vol.142, no.1, pp.47– 54, 1982.
15. N. J. Dudley, “A systematic review of the ultrasound estimation of fetal weight,” Ultrasound in Obstetrics and Gynecology, vol.25, no.1, pp.80– 89, 2005.
16. L. Breiman, “Random Forests,” Machine Learning, vol.45, no.1, pp.5– 32, 2001.
17. J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of Statistics, vol.29, no.5, pp.1189–1232, 2001.
18. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” J. Artificial Intelligence Research, vol.16, pp.321–357, 2002.
19. Niculescu-Mizil and R. Caruana, “Predicting good probabilities with supervised learning,” in Proc. 22nd Int. Conf. Machine Learning (ICML), pp.625–632, 2005.
20. L. I. Kuncheva and C. J. Whitaker, “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,” Machine Learning, vol.51, no.2, pp.181–207, 2003.
21. Q. McNemar, “Note on the sampling error of the difference between correlated proportions or percentages,” Psychometrika, vol.12, no.2, pp.153–157, 1947.
22. G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol.18, no.7, pp.1527–1554, 2006.
23. H. Sahin and A. Subasi, “Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques,” Applied Soft Computing, vol.33, pp.231–238, Aug. 2015.
24. L. Lin, I. A. Dekkers, Q. Tao, and H. J. Lamb, “Novel artificial neural network and linear regression based equation for estimating visceral adipose tissue volume,” Clinical Nutrition, vol.39, no.10, pp.3182– 3188, 2020.
25. S. Badshah et al., “Risk factors for low birth weight in the public hospitals at Peshawar, NWFP-Pakistan,” BMC Public Health, vol.8, p.197, 2008.
26. H. G. Mengesha, A. D. Wuneh, B. Weldearegawi, and S. D. Misgina, “Low birth weight and macrosomia in Tigray, Northern Ethiopia: who are the mothers at risk?,” BMC Pediatrics, vol.17, no.1, p.144, 2017.
27. T. Henriksen, “The macrosomic fetus: a challenge in current obstetrics,” Acta Obstetricia et Gynecologica Scandinavica, vol.87, no.2, pp.134– 145, 2008.
28. M. Bernstein and P. M. Catalano, “Influence of fetal fat on the ultrasound estimation of fetal weight in diabetic mothers,” Obstetrics and Gynecology, vol.79, no.4, pp.561–563, 1992.
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