{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T18:18:02Z","timestamp":1771352282382,"version":"3.50.1"},"reference-count":37,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Arba Minch University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate classification of child delivery mode is crucial for improving maternal and neonatal health. In developing countries like Ethiopia, clinical assessments alone often result in misguided medical interventions. Even though machine learning in healthcare has brought promises, the various algorithms along with different real-world datasets perform differently. Hence, the objective of this study was to develop a machine learning model for predicting child delivery mode based on real data. The study followed experimental and exploratory research design utilizing 1,072 antenatal records from Arba Minch General Hospital and Birbir Health Center, Ethiopia. 16 attributes were considered including the outcome, mode of delivery. Predictors included sociodemographic and clinical variables such as age, weight, blood pressure, previous CS, and fetal presentation. Five machine learning algorithms including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and CatBoost were trained and evaluated using hold-out validation. Additionally, a recent deep learning model, TabPFN, and Long Short-Term Memory were examined to expand the exploration. The results showed that RF (93.1\u202f%) achieved the highest accuracy. TabPFN scored the second best accuracy score (92.5\u202f%), demonstrating its potential on smaller tabular data. LSTM performed better than SVM and LR highlighting the consideration of the inherent temporal characteristics of the\u00a0data.<\/jats:p>","DOI":"10.1515\/jisys-2025-0247","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T17:56:01Z","timestamp":1771350961000},"source":"Crossref","is-referenced-by-count":0,"title":["Child delivery mode prediction: exploring machine learning algorithms and dataset organizations"],"prefix":"10.1515","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0827-8837","authenticated-orcid":false,"given":"Abay Hailemariam","family":"Dayssa","sequence":"first","affiliation":[{"name":"Faculty of Computing and Software Engineering , 128157 Arba Minch University , Arba Minch , PoBox\u00a021 , South Ethiopia , Ethiopia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5070-2727","authenticated-orcid":false,"given":"Mesay Samuel","family":"Gondere","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Software Engineering , 128157 Arba Minch University , Arba Minch , PoBox\u00a021 , South Ethiopia , Ethiopia"}]}],"member":"374","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"2026021717555731902_j_jisys-2025-0247_ref_011","doi-asserted-by":"crossref","unstructured":"M. N. Islam, T. Mahmud, N. I. Khan, S. N. Mustafina, and A. N. Islam, \u201cExploring machine learning algorithms to find the best features for predicting modes of childbirth,\u201d IEEE Access, vol.\u00a09, pp.\u00a01680\u20131692, 2020, https:\/\/doi.org\/10.1109\/ACCESS.2020.3045469.","DOI":"10.1109\/ACCESS.2020.3045469"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_014","doi-asserted-by":"crossref","unstructured":"M. Kowsher, N. J. Prottasha, A. Tahabilder, H. Kaiser, M. Abdur-Rakib, and M. S. Alam, \u201cPredicting the appropriate mode of childbirth using machine learning algorithm,\u201d Int. J.\u00a0Adv. Comput. Sci. Appl., vol. 12, no. 5, pp. 700\u2013708, 2021, https:\/\/doi.org\/10.14569\/IJACSA.2021.0120582.","DOI":"10.14569\/IJACSA.2021.0120582"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_006","doi-asserted-by":"crossref","unstructured":"F. H. Bitew, S. H. Nyarko, L. Potter, and C. S. Sparks, \u201cMachine learning approach for predicting under-five mortality determinants in Ethiopia: Evidence from the 2016 Ethiopian demographic and health survey,\u201d Genus, vol.\u00a076, no. 1, p.\u00a037, 2020, https:\/\/doi.org\/10.1186\/s41118-020-00106-2.","DOI":"10.1186\/s41118-020-00106-2"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_019","doi-asserted-by":"crossref","unstructured":"M. W. Moreira, J.\u00a0J. Rodrigues, N. Kumar, J. Niu, and I. Woungang, \u201cPerformance assessment of decision tree-based predictive classifiers for risk pregnancy care,\u201d in GLOBECOM 2017\u20132017 IEEE Global Communications Conference, 2017, pp.\u00a01\u20135, https:\/\/doi.org\/10.1109\/GLOCOM.2017.8254451.","DOI":"10.1109\/GLOCOM.2017.8254451"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_024","unstructured":"N. Rekha and S. Kambli, \u201cModel for predicting risk levels in maternal healthcare,\u201d Int. J.\u00a0Adv. Res. Innovat. Ideas Educ., vol.\u00a08, no. 6, pp.\u00a01633\u20131637, 2022."},{"key":"2026021717555731902_j_jisys-2025-0247_ref_025","doi-asserted-by":"crossref","unstructured":"N. Rezaei, M. Amani, H. Asgharpoor, V. Mehrnoush, F. Darsareh, and A. Nikmanesh, \u201cMachine learning approach to predict emergency cesarean sections among nulliparous women,\u201d Cureus, vol. 17, no. 8, pp. 1\u201312, 2025, https:\/\/doi.org\/10.7759\/cureus.90501.","DOI":"10.7759\/cureus.90501"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_034","doi-asserted-by":"crossref","unstructured":"E. Taeidi, A. Ranjbar, F. Montazeri, V. Mehrnoush, and F. Darsareh, \u201cMachine learning-based approach to predict intrauterine growth restriction,\u201d Cureus, vol. 15, no. 7, pp. 1\u20136, 2023, https:\/\/doi.org\/10.7759\/cureus.41448.","DOI":"10.7759\/cureus.41448"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_023","doi-asserted-by":"crossref","unstructured":"A. Ranjbar, E. Taeidi, V. Mehrnoush, N. Roozbeh, and F. Darsareh, \u201cMachine learning models for predicting pre-eclampsia: A systematic review protocol,\u201d BMJ Open, vol.\u00a013, no. 9, 2023, p.\u00a0e074705, https:\/\/doi.org\/10.1136\/bmjopen-2023-074705.","DOI":"10.1136\/bmjopen-2023-074705"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_027","doi-asserted-by":"crossref","unstructured":"S. Safarzadeh, N. S. Ardabili, M. V. Farashah, N. Roozbeh, and F. Darsareh, \u201cPredicting mother and newborn skin-to-skin contact using a machine learning approach,\u201d BMC Pregnancy Childbirth, vol.\u00a025, no. 1, p.\u00a0182, 2025, https:\/\/doi.org\/10.1186\/s12884-025-07313-9.","DOI":"10.1186\/s12884-025-07313-9"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_026","doi-asserted-by":"crossref","unstructured":"N. Roozbeh, F. Montazeri, M. V. Farashah, V. Mehrnoush, and F. Darsareh, \u201cProposing a machine learning-based model for predicting nonreassuring fetal heart,\u201d Sci. Rep., vol.\u00a015, no. 1, p.\u00a07812, 2025, https:\/\/doi.org\/10.1038\/s41598-025-92810-2.","DOI":"10.1038\/s41598-025-92810-2"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_003","doi-asserted-by":"crossref","unstructured":"M. Banaei, N. Roozbeh, F. Darsareh, V. Mehrnoush, M. S. V. Farashah, and F. Montazeri, \u201cUtilizing machine learning to predict the risk factors of episiotomy in parturient women,\u201d AJOG Global Rep., vol.\u00a05, no. 1, p.\u00a0100420, 2025, https:\/\/doi.org\/10.1016\/j.xagr.2024.100420.","DOI":"10.1016\/j.xagr.2024.100420"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_005","doi-asserted-by":"crossref","unstructured":"A. Bertini, R. Salas, S. Chabert, L. Sobrevia, and F. Pardo, \u201cUsing machine learning to predict complications in pregnancy: A systematic review,\u201d Front. Bioeng. Biotechnol., vol.\u00a09, p.\u00a0780389, 2022, https:\/\/doi.org\/10.3389\/fbioe.2021.780389.","DOI":"10.3389\/fbioe.2021.780389"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_016","unstructured":"Macrotrends, Ethiopia Maternal Mortality Rate, Historical Chart & Data, n.d."},{"key":"2026021717555731902_j_jisys-2025-0247_ref_022","unstructured":"W. H. Organization and Others, Maternal Mortality: The Urgency of a Systemic and Multisectoral Approach in Mitigating Maternal Deaths in Africa, n.d. 2023."},{"key":"2026021717555731902_j_jisys-2025-0247_ref_031","doi-asserted-by":"crossref","unstructured":"Y. Sinebo, T. Ejajo, A. Girma, and T. Yohannes, \u201cHigh-risk fertility behavior and associated factors among mothers attending antenatal care at public health facilities in Hossana town, central Ethiopia region: Facility based cross sectional study,\u201d BMC Pregnancy Childbirth, vol.\u00a025, no. 1, p.\u00a031, 2025, https:\/\/doi.org\/10.1186\/s12884-024-07125-3.","DOI":"10.1186\/s12884-024-07125-3"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_036","doi-asserted-by":"crossref","unstructured":"Z. Tenaw, Z. Y. Kassa, G. Kassahun, and A. Ayenew, \u201cMaternal preference, mode of delivery and associated factors among women who gave birth at public and private hospitals in Hawassa city, southern Ethiopia,\u201d Ann. Glob. Health, vol.\u00a085, no. 1, p.\u00a0115, 2019, https:\/\/doi.org\/10.5334\/aogh.2578.","DOI":"10.5334\/aogh.2578"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_004","doi-asserted-by":"crossref","unstructured":"R. Beri, M. K. Dubey, A. Gehlot, and R. Raj, \u201cPerformance assessment of supervised learning techniques for caesarean rate prediction,\u201d 2020. Available at SSRN 3517430: https:\/\/doi.org\/10.2139\/ssrn.3517430.","DOI":"10.2139\/ssrn.3517430"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_015","doi-asserted-by":"crossref","unstructured":"A. Kuanar, A. Akbar, P. Sujata, and D. Kar, \u201cDeep neural network (DNN) modelling for prediction of the mode of delivery,\u201d Eur. J.\u00a0Obstet. Gynecol. Reprod. Biol., vol.\u00a0297, pp.\u00a0241\u2013248, 2024, https:\/\/doi.org\/10.1016\/j.ejogrb.2024.04.012.","DOI":"10.1016\/j.ejogrb.2024.04.012"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_020","doi-asserted-by":"crossref","unstructured":"N. Nasrat and K. Lavangnananda, \u201cApplication of machine learning in assignment of child delivery service in Afghanistan,\u201d in 2021 18th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTICON), 2021, pp.\u00a01172\u20131175, https:\/\/doi.org\/10.1109\/ECTI-CON51831.2021.9454691.","DOI":"10.1109\/ECTI-CON51831.2021.9454691"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_001","doi-asserted-by":"crossref","unstructured":"A. Abdulmumini, G. Obunadike, and E. Jiya, \u201cPredictive model for child delivery,\u201d FUDMA J.\u00a0Sci., vol.\u00a06, no. 1, pp.\u00a0141\u2013145, 2022, https:\/\/doi.org\/10.33003\/fjs-2022-0601-885.","DOI":"10.33003\/fjs-2022-0601-885"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_002","doi-asserted-by":"crossref","unstructured":"M. S. B. Alam, M. J. Patwary, and M. Hassan, \u201cBirth mode prediction using bagging ensemble classifier: A case study of Bangladesh,\u201d in 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), 2021, pp.\u00a095\u201399, https:\/\/doi.org\/10.1109\/ICICT4SD50815.2021.9396909.","DOI":"10.1109\/ICICT4SD50815.2021.9396909"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_029","unstructured":"M. Sekireddy and S. Thatimakula, \u201cPrediction of child birth delivery mode using hybrid-boosting ensemble machine learning model,\u201d in Proceedings of the 11th International Conference on Applied Innovations in it, (ICAIIT), 2023, pp.\u00a0101\u2013105, https:\/\/doi.org\/10.25673\/112999."},{"key":"2026021717555731902_j_jisys-2025-0247_ref_037","doi-asserted-by":"crossref","unstructured":"J.-H. Wang and S.-J. Lou, \u201cPredicting the success rate of natural spontaneous delivery through deep learning,\u201d in 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2019, pp.\u00a01\u20132, https:\/\/doi.org\/10.1109\/ICCE-TW46550.2019.8992022.","DOI":"10.1109\/ICCE-TW46550.2019.8992022"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_028","doi-asserted-by":"crossref","unstructured":"I. H. Sarker, \u201cMachine learning: Algorithms, real-world applications and research directions,\u201d SN Comput. Sci., vol.\u00a02, no. 3, p.\u00a0160, 2021, https:\/\/doi.org\/10.1007\/s42979-021-00592-x.","DOI":"10.1007\/s42979-021-00592-x"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_032","doi-asserted-by":"crossref","unstructured":"P. Singh, \u201cSystematic review of data-centric approaches in artificial intelligence and machine learning,\u201d Data Sci. Manag., vol.\u00a06, no. 3, pp.\u00a0144\u2013157, 2023, https:\/\/doi.org\/10.1016\/j.dsm.2023.06.001.","DOI":"10.1016\/j.dsm.2023.06.001"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_035","doi-asserted-by":"crossref","unstructured":"J. Tao, Z. Yuan, L. Sun, K. Yu, and Z. Zhang, \u201cFetal birthweight prediction with measured data by a temporal machine learning method,\u201d BMC Med. Inf. Decis. Making, vol.\u00a021, no. 1, p.\u00a026, 2021, https:\/\/doi.org\/10.1186\/s12911-021-01388-y.","DOI":"10.1186\/s12911-021-01388-y"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_021","unstructured":"A. Natarajan, V. Rajasekaran, A. Shiek, L. Ch Narasimha Rao, V. N. Zainil, and R. J. Babu, \u201cPredicting the childbirth mode using exhaustivefeature selection and machine learning techniques,\u201d Int. J.\u00a0Eng. Res. Technol. (IJERT), vol. 12, no. 6, pp. 307\u2013310, 2023."},{"key":"2026021717555731902_j_jisys-2025-0247_ref_013","doi-asserted-by":"crossref","unstructured":"G. Kopanitsa, O. Metsker, and S. Kovalchuk, \u201cMachine learning methods for pregnancy and childbirth risk management,\u201d J.\u00a0Personalized Med., vol.\u00a013, no. 6, p.\u00a0975, 2023, https:\/\/doi.org\/10.3390\/jpm13060975.","DOI":"10.3390\/jpm13060975"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_007","doi-asserted-by":"crossref","unstructured":"A. D. R. Fern\u00e1ndez, D. R. Fern\u00e1ndez, and M. T. P. S\u00e1nchez, \u201cPrediction of the mode of delivery using artificial intelligence algorithms,\u201d Comput. Methods Progr. Biomed., vol.\u00a0219, p.\u00a0106740, 2022, https:\/\/doi.org\/10.1016\/j.cmpb.2022.106740.","DOI":"10.1016\/j.cmpb.2022.106740"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_018","doi-asserted-by":"crossref","unstructured":"K. Michalitsi, et al., \u201cArtificial intelligence in predicting the mode of delivery: A systematic review,\u201d Cureus, vol. 16, no. 9, pp. 1\u201313, 2024, https:\/\/doi.org\/10.7759\/cureus.69115.","DOI":"10.7759\/cureus.69115"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_008","unstructured":"L. M. Gladence, M. Karthi, and V. M. Anu, \u201cA statistical comparison of logistic regression and different Bayes classification methods for machine learning,\u201d ARPN J.\u00a0Eng. Appl. Sci., vol.\u00a010, no. 14, pp.\u00a05947\u20135953, 2015."},{"key":"2026021717555731902_j_jisys-2025-0247_ref_033","unstructured":"J.\u00a0V. Siva and K. Reddeppa, \u201cPredicting modes of child birth using machine learning algorithms,\u201d Int. J.\u00a0All Res. Educ. Sci. Methods (IJARESM), vol.\u00a010, no. 5, pp.\u00a02004\u20132008, 2022."},{"key":"2026021717555731902_j_jisys-2025-0247_ref_010","doi-asserted-by":"crossref","unstructured":"A. A. Ibrahim, R. L. Ridwan, M. M. Muhammed, R. O. Abdulaziz, and G. A. Saheed, \u201cComparison of the catboost classifier with other machine learning methods,\u201d Int. J.\u00a0Adv. Comput. Sci. Appl., vol.\u00a011, no. 11, pp.\u00a0738\u2013748, 2020, https:\/\/doi.org\/10.14569\/IJACSA.2020.0111190.","DOI":"10.14569\/IJACSA.2020.0111190"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_009","unstructured":"N. Hollmann, S. M\u00fcller, K. Eggensperger, and F. Hutter, \u201cTabpfn: A transformer that solves small tabular classification problems in a second,\u201d arXiv preprint arXiv:2207.01848, 2022, https:\/\/doi.org\/10.48550\/arXiv.2207.01848."},{"key":"2026021717555731902_j_jisys-2025-0247_ref_012","doi-asserted-by":"crossref","unstructured":"F. Karim, S. Majumdar, H. Darabi, and S. Harford, \u201cMultivariate LSTM-FCNS for time series classification,\u201d Neural Netw., vol.\u00a0116, pp.\u00a0237\u2013245, 2019, https:\/\/doi.org\/10.1016\/j.neunet.2019.04.014.","DOI":"10.1016\/j.neunet.2019.04.014"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_030","doi-asserted-by":"crossref","unstructured":"M. Y. Shams, A. M. Elshewey, E.-S. M. El-Kenawy, A. Ibrahim, F. M. Talaat, and Z. Tarek, \u201cWater quality prediction using machine learning models based on grid search method,\u201d Multimed. Tool. Appl., vol.\u00a083, no. 12, pp.\u00a035307\u201335334, 2024, https:\/\/doi.org\/10.1007\/s11042-023-16737-4.","DOI":"10.1007\/s11042-023-16737-4"},{"key":"2026021717555731902_j_jisys-2025-0247_ref_017","unstructured":"S. Matheswari, J. Meaga, S. Nishani, and S. Vizhiyarasi, \u201cPredicting childbirth modes: A comparative analysis of machine learning algorithm,\u201d Int. J.\u00a0Sci. Res. 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