Unveiling the Unborn: Advancing Fetal Health Classification through
  Machine Learning
Sujith K Mandala
Fetal health classification is a critical task in obstetrics, enabling early
identification and management of potential health problems. However, it remains
challenging due to data complexity and limited labeled samples. This research
paper presents a novel machine-learning approach for fetal health
classification, leveraging a LightGBM classifier trained on a comprehensive
dataset. The proposed model achieves an impressive accuracy of 98.31% on a test
set. Our findings demonstrate the potential of machine learning in enhancing
fetal health classification, offering a more objective and accurate assessment.
Notably, our approach combines various features, such as fetal heart rate,
uterine contractions, and maternal blood pressure, to provide a comprehensive
evaluation. This methodology holds promise for improving early detection and
treatment of fetal health issues, ensuring better outcomes for both mothers and
babies. Beyond the high accuracy achieved, the novelty of our approach lies in
its comprehensive feature selection and assessment methodology. By
incorporating multiple data points, our model offers a more holistic and
reliable evaluation compared to traditional methods. This research has
significant implications in the field of obstetrics, paving the way for
advancements in early detection and intervention of fetal health concerns.
Future work involves validating the model on a larger dataset and developing a
clinical application. Ultimately, we anticipate that our research will
revolutionize the assessment and management of fetal health, contributing to
improved healthcare outcomes for expectant mothers and their babies.