PREDICTIVE MODEL FOR CHILD DELIVERY

Authors

Keywords:

Safe delivery, Caesarian section, Neural network, Random forest, Naïve Bayes

Abstract

Antenatal care is an essential period in which medical experts examines pregnant women to prepare them for proper child delivery. Choosing or knowing the likely mode of child delivery is essential both for the mother and the medical team. It helps in proper preparation for labour and any possible complication that could arise. There are also chances of reducing maternal or child mortality. However, the decision on which of the options is appropriate is sometimes difficult due to several parameters and variable. Analyzing Obstetric and mode of delivery for pregnant woman is tedious, therefore, this work used data from three medical facilities in Katsina State and apply three machine leaning algorithms to predict the most appropriate mode of child delivery. The work was implemented using python programming language software. The result of the work has shown that random forest algorithm performs better with accuracy result as   precision was 0.918, recall of 0.715 and 0.896 for Spontaneous Vagina delivery and 0.716 for precision, 0.929 for recall and 0.896 for Caesarian section mode

Dimensions

Kumar, et al. (2014) "Dense phenotyping from electronic health records enables machine-learning-based prediction of preterm birth."

Pereira, Joana Carvalho, Maria João Ferreira, Graça Rodrigues, and Olímpia Do Carmo. (2015). Value of Bishop score and ultrasound cervical length measurement in the prediction of cesarean delivery. Journal of Obstetrics and Gynaecology Research 39, 9, 1391–1396

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Lakshmi, Lijue, et al. "Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor." Computer Methods and Programs in Biomedicine 196 (2016): 105624.

Khazardoost Birara and Yirgu Gebrehiwot. (2016). Factors associated with success of vaginal birth after one caesarean section (VBAC) at three teaching hospitals in Addis Ababa, Ethiopia: a case control study. BMC pregnancy and childbirth 13, 1 (2016), 31.

Published

31-03-2022

How to Cite

PREDICTIVE MODEL FOR CHILD DELIVERY. (2022). FUDMA JOURNAL OF SCIENCES, 6(1), 141-145. https://doi.org/10.33003/fjs-2022-0601-885

Issue

Vol. 6 No. 1 (2022): FUDMA Journal of Sciences - Vol. 6 No. 1

Section

Research Articles
Copyright & Licensing

How to Cite

PREDICTIVE MODEL FOR CHILD DELIVERY. (2022). FUDMA JOURNAL OF SCIENCES, 6(1), 141-145. https://doi.org/10.33003/fjs-2022-0601-885