An Analytical Study of Improved Machine Learning Approaches for Predicting Mode of Delivery
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9562-2_60
Reference8 articles.
1. Ricciardi C, Improta G, Amato F, Cesarelli G, Romano M (2020) Classifying the type of delivery from cardiotocographic signals: a machine learning approach. Comput Methods Programs Biomed 196:105712
2. Andersen BR, Ammitzbøll I, Hinrich J, Lehmann S, Ringsted CV, Løkkegaard ECL, Tolsgaard MG (2022) Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study. BMJ Open 12(3):e049046
3. Islam MS, Awal MA, Laboni JN, Pinki FT, Karmokar S, Mumenin KM, Mirjalili S (2022) HGSORF: henry gas solubility optimization-based random forest for c-section prediction and XAI-based cause analysis. Comput Biol Med 147:105671
4. Sabu SP, Suresh Kumar S, Sajini BN, Anitha Kumari KR (2000) Caesarean section delivery in Kerala, India: evidence from a National Family Health Survey. Soc Sci Med 51(4):511–521. https://doi.org/10.1016/S0277-9536(99)00491-8
5. Diema Konlan K, Baku EK, Japiong M, Dodam Konlan K, Amoah RM (2019) Reasons for women’s choice of elective caesarian section in Duayaw Nkwanta Hospital. J Preg
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