{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T11:27:37Z","timestamp":1787225257580,"version":"build-2736575974"},"reference-count":36,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Objective: This study aimed to develop machine learning (ML)-based models for the early prediction of macrosomia using only maternal sociodemographic and obstetric data. Methods: This retrospective study included 100 pregnant women who delivered at the Obstetric Clinic of Eskisehir City Hospital between January 2022 and December 2023. Participants were classified as nulliparous (n = 48) or parous (n = 52) and further categorized according to the presence or absence of fetal macrosomia (birth weight &gt; 4000 g). Predictor variables included maternal age, body mass index (BMI), gravida, smoking status, history of diabetes mellitus, and hypertension; previous birth weight was additionally included in the parous model. Separate machine learning models were developed for nulliparous and parous women using Extra Trees Classifier, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting (XGB) Classifier, Random Forest, and Logistic Regression. The dataset was randomly divided into training (80%) and testing (20%) subsets. Internal validation was performed using 10-fold cross-validation within the training dataset to optimize model performance and reduce overfitting. Given the relatively small sample size, this study was designed as a pilot exploratory investigation. Results: Overall, 48 nulliparous and 52 parous mothers were included in the study. Among the nulliparous women, 22 (45.8%) had macrosomic newborns, whereas 26 (54.2%) had normal birthweight newborns. Among parous women, 28 (53.8%) had macrosomic newborns, while 24 (46.2%) had normal birthweight newborns. For nulliparous mothers, the XGB Classifier achieved the highest accuracy (80%) and AUC-ROC (82.2%), demonstrating robust predictive performance. For parous mothers, the XGB Classifier again outperformed other ML models, achieving an accuracy of 72.7% and an AUC-ROC of 83.3% in predicting macrosomia. Conclusions: This study highlights the feasibility of ML-based decision support systems in obstetrics, particularly in low-resource settings, to predict macrosomia using readily available maternal characteristics. As a pilot study, these findings should be interpreted cautiously and require validation in larger multicenter cohorts before clinical implementation.<\/jats:p>","DOI":"10.3390\/diagnostics16162661","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T10:55:18Z","timestamp":1787223318000},"page":"2661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Prediction of Fetal Macrosomia Using Maternal: A Pilot Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0190-977X","authenticated-orcid":false,"given":"Tu\u011fba","family":"Tahta","sequence":"first","affiliation":[{"name":"Midwifery Department, Faculty of Health Sciences, Ankara Medipol University, Ankara 06050, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5297-4462","authenticated-orcid":false,"given":"Zafer","family":"B\u00fct\u00fcn","sequence":"additional","affiliation":[{"name":"Ho\u015fnudiye Mah. Ay\u015fen Sokak Dorya Rezidans, Ablok no:28\/77, Eski\u015fehir 26130, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00d6zer","family":"\u00c7elik","sequence":"additional","affiliation":[{"name":"Vocational School of Information Technologies, Anadolu University, Eski\u015fehir 26470, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-3035","authenticated-orcid":false,"given":"Ece","family":"Ak\u00e7a Salik","sequence":"additional","affiliation":[{"name":"Department of Gynecology and Obstetrics, Eskisehir City Hospital, Eski\u015fehir 26080, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4277-3960","authenticated-orcid":false,"given":"Yeliz","family":"Kaya","sequence":"additional","affiliation":[{"name":"Department of Gynecology and Obstetrics Nursing, Faculty of Health Sciences, Eski\u015fehir Osmangazi University, Eski\u015fehir 26040, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mennickent, D., Rodr\u00edguez, A., Opazo, M.C., Riedel, C.A., Castro, E., Eriz-Salinas, A., Appel-Rubio, J., Aguayo, C., Damiano, A.E., and Guzm\u00e1n-Guti\u00e9rrez, E. (2023). Machine learning applied in maternal and fetal health: A narrative review focused on pregnancy diseases and complications. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1130139"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1016\/0002-9378(91)90424-P","article-title":"Shoulder dystocia: Should the fetus weighing \u22654000 grams be delivered by cesarean section?","volume":"165","author":"Langer","year":"1991","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/S0301-2115(01)00416-X","article-title":"Perinatal and maternal outcomes of fetal macrosomia","volume":"99","author":"Oral","year":"2001","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"62","DOI":"10.11604\/pamj.2017.26.62.11440","article-title":"Frequency of fetal macrosomia and associated risk factors in pregnancies without gestational diabetes mellitus","volume":"26","author":"Usta","year":"2017","journal-title":"Pan Afr. Med. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lewandowska, M. (2021). The role of maternal weight in the hierarchy of macrosomia predictors: Overall effect of analysis of three prediction indicators. Nutrients, 13.","DOI":"10.3390\/nu13030801"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"109","DOI":"10.3109\/14767058.2016.1163679","article-title":"Pre-pregnancy body mass index and macrosomia in a Canadian birth cohort","volume":"30","author":"Vinturache","year":"2017","journal-title":"J. Matern. Fetal Neonatal Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tela, F.G., Bezabih, A.M., Adhanu, A.K., and Tekola, K.B. (2019). Fetal macrosomia and associated factors among singleton live-births in private clinics in Mekelle city. BMC Pregnancy Childbirth, 19.","DOI":"10.1186\/s12884-019-2379-3"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Juan, J., Wei, Y., Song, G., Su, R., Chen, X., Shan, R., Yan, J., Xiao, M., Li, Y., and Cui, S. (2022). Risk factors for macrosomia in multipara: A multi-center retrospective study. Children, 9.","DOI":"10.3390\/children9070935"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lewandowska, M. (2021). Maternal obesity and risk of macrosomia and fetal growth restriction. Nutrients, 13.","DOI":"10.3390\/nu13041213"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107811","DOI":"10.1016\/j.diabres.2019.107811","article-title":"Effects of pre-gestational and gestational diabetes on macrosomia","volume":"155","author":"Yang","year":"2019","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2232072","DOI":"10.1080\/14767058.2023.2232072","article-title":"Establishment of a nomogram model to predict macrosomia in GDM","volume":"36","author":"Sun","year":"2023","journal-title":"J. Matern. Fetal Neonatal Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1080\/15513815.2019.1675831","article-title":"Maternal neck\/waist circumferences and macrosomia","volume":"40","author":"Hancerliogullari","year":"2021","journal-title":"Fetal Pediatr. Pathol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liu, H., Wang, J., Hu, X., Wang, A., Nie, Z., Xu, H., Li, J., Xin, H., and Zhang, J. (2022). Development and validation of a predictive model for macrosomia. Front. Endocrinol., 13.","DOI":"10.3389\/fendo.2022.1019234"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zou, Y., Zhang, Y., Yin, Z., Wei, L., Lv, B., and Wu, Y. (2021). Nomogram model for macrosomia in GDM. BMC Pregnancy Childbirth, 21.","DOI":"10.1186\/s12884-021-04049-0"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Du, J., Zhang, X., Chai, S., Zhao, X., Sun, J., Yuan, N., Yu, X., and Zhang, Q. (2022). Risk prediction of macrosomia: A case-control study. BMC Pregnancy Childbirth, 22.","DOI":"10.1186\/s12884-022-04706-y"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jing, G., Huwei, S., Chao, C., Lei, C., Ping, W., Zhongzhou, X., Sen, Y., Jiayuan, C., Ruiyao, C., and Lu, L. (2022). Predictive model of macrosomic birth using clinical data. BMC Pregnancy Childbirth, 22.","DOI":"10.1186\/s12884-022-04981-9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.7150\/ijms.49857","article-title":"Predictability of macrosomic birth in hyperglycemic mothers","volume":"18","author":"Hur","year":"2021","journal-title":"Int. J. Med. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ulusoy, C.O., Kurt, A., Y\u0131ld\u0131z, A.G., Akbulut, \u00d6.V., Baran, G.K., and Engin \u00dcst\u00fcn, Y. (2025). Machine learning-based prediction of shoulder dystocia in pregnancies without suspected macrosomia using fetal biometric ratios. J. Clin. Med., 14.","DOI":"10.3390\/jcm14155240"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e108019","DOI":"10.1136\/bmjopen-2025-108019","article-title":"Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: A study protocol","volume":"15","author":"Mohammadi","year":"2025","journal-title":"BMJ Open"},{"key":"ref_20","first-page":"e90501","article-title":"Machine Learning Approach to Predict Emergency Cesarean Sections Among Nulliparous Women","volume":"17","author":"Rezaei","year":"2025","journal-title":"Cureus"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Abdi, F., Roozbeh, N., Darsareh, F., Mehrnoush, V., Farashah, M.S.V., and Montazeri, F. (2025). Developing a prognostic model for predicting preterm birth using a machine learning algorithm. BMC Pregnancy Childbirth, 25.","DOI":"10.1186\/s12884-025-08136-4"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"20914","DOI":"10.1038\/s41598-025-06651-0","article-title":"Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission","volume":"15","author":"Malakooti","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mehrnoush, V., Haghighat, A., Nami, A., Rezaei, N., Darsareh, F., Montazeri, F., and Saffari, M. (2026). The power of machine learning models in predicting gestational diabetes mellitus. BMC Pregnancy Childbirth, 26.","DOI":"10.1186\/s12884-026-08856-1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.gocm.2023.07.002","article-title":"Predicting risk of postpartum hemorrhage using machine learning approach: A systematic review","volume":"3","author":"Ranjbar","year":"2023","journal-title":"Gynecol. Obstet. Clin. Med."},{"key":"ref_25","first-page":"556233","article-title":"Prediction of Renal Cancer Recurrence using Artificial Intelligence: A Systematic Review","volume":"28","author":"Shahrour","year":"2025","journal-title":"Cancer Ther. Oncol. Int. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"100099","DOI":"10.1016\/j.crphys.2023.100099","article-title":"Machine learning in obstetrics","volume":"6","author":"Arain","year":"2023","journal-title":"Curr. Res. Physiol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhu, S., Zhao, M., Ma, L., Wang, C., Sun, X., Feng, Y., Wu, Y., Zheng, Z., and Zhang, L. (2025). Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: A retrospective study in China. BMC Pregnancy Childbirth, 25.","DOI":"10.1186\/s12884-025-07239-2"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, J., Liu, R., Zhou, X., Zeng, D., She, Q., Ouyang, S., and Liu, L. (2026). Development and validation of machine learning-based model for macrosomia and spontaneous preterm birth: A retrospective cohort study. Int. J. Gynecol. Obstet., 1\u201310.","DOI":"10.1002\/ijgo.71182"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, F., Wang, Y., Ji, X., and Wang, Z. (2022). Machine learning prediction of macrosomia using random forest. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19063245"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108665","DOI":"10.1016\/j.compbiomed.2024.108665","article-title":"Longitudinal learning for macrosomia prediction using RNN","volume":"177","author":"Liu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, Z., Han, N., Su, T., Ji, Y., Bao, H., Zhou, S., Luo, S., Wang, H., Liu, J., and Wang, H.-J. (2022). Machine learning predictors of birth weight. Front. Pediatr., 10.","DOI":"10.3389\/fped.2022.899954"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ye, S., Zhang, H., Shi, F., Guo, J., Wang, S., and Zhang, B. (2020). Ensemble learning for macrosomia prediction. J. Clin. Med., 9.","DOI":"10.3390\/jcm9020380"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3859","DOI":"10.1080\/14767058.2018.1484090","article-title":"Predictive model for macrosomia","volume":"32","author":"Shigemi","year":"2019","journal-title":"J. Matern. Fetal Neonatal Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1080\/14767058.2019.1668922","article-title":"Prediction model for GDM with macrosomia","volume":"34","author":"Kang","year":"2021","journal-title":"J. Matern. Fetal Neonatal Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cui, M., Yang, H., Wang, B., Zhang, J., Chu, Q., Zhang, C., Yao, Z., and Wang, L. (2026). Predicting macrosomia and low birth weight with interpretable machine learning. BMC Pregnancy Childbirth, 26.","DOI":"10.1186\/s12884-026-08667-4"},{"key":"ref_36","unstructured":"World Health Organization (2024, June 17). Antenatal Care Coverage. Available online: https:\/\/www.who.int\/data\/gho\/data\/indicators\/indicator-details\/GHO\/antenatal-care-coverage-at-least-four-visits."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/16\/2661\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T11:01:47Z","timestamp":1787223707000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/16\/2661"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,20]]},"references-count":36,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["diagnostics16162661"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16162661","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,20]]}}}