Abstract:
Fetal health is a significant public health concern, and monitoring it is crucial for mothers
to ensure a healthy baby. In Ethiopia, a partograph is used during delivery to monitor labor
conditions, maternal health, and fetal health presentation. Fetal and maternal mortality is a
significant public health issue, particularly in low-income countries like Ethiopia, due to
improper monitoring of fetal and maternal health during pregnancy and childbirth, resulting
from inadequate record keeping. The researcher motivated because of in Ethiopia
specifically the selected study area is experiencing an increase in maternal and fetal
mortality due to complications from pregnancy and childbirth increasing in day to day’s
activity. Pregnant women often die from underlying diseases, which are difficult to treat due
to potential harm to the developing fetus. Continuous follow-up could fill this gap by
providing accurate fetal state classification. The main objective of the study was to develop
a classification model of the normal, suspect, and risky progress of feto-maternal and labor
conditions based on a partograph presentation of recorded labor progress using machine
learning approaches in the case of Mizan Tepi University teaching hospital. After collecting
a partogram record of data required data preparation for analysis, data scaling to
standardize the data, and preprocessing steps applied using the principal component
analysis feature extraction method to handle big dimensional data keeping as much
information in the data as possible. The classification was done with two scenarios with
default hyperparameter and applying the model's respective hyperparameter tuning, such
as CV and Gridsearch. Therefore support vector machine, random forest, and extreme
gradient boosting classification machine learning algorithm were trained on the dataset and
evaluated with classification evaluation metrics. Finally, the support vector machine scored
higher on both scenarios and scored 99.19% after performance improvement was done with
cross-validation and hyperparameter tuning. The developed models have the capability of
categorizing fetal health status. It is recommended that collecting a large amount of data for
all classes, and developing a more reliable classification model, and deployment in the
future.
