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Fetal health classification is a critical task in obstetrics, enabling earlyidentification and management of potential health problems. However, it remainschallenging due to data complexity and limited labeled samples. This researchpaper presents a novel machine-learning approach for fetal healthclassification, leveraging a LightGBM classifier trained on a comprehensivedataset. The proposed model achieves an impressive accuracy of 98.31% on a testset. Our findings demonstrate the potential of machine learning in enhancingfetal 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 comprehensiveevaluation. This methodology holds promise for improving early detection andtreatment of fetal health issues, ensuring better outcomes for both mothers andbabies. Beyond the high accuracy achieved, the novelty of our approach lies inits comprehensive feature selection and assessment methodology. Byincorporating multiple data points, our model offers a more holistic andreliable evaluation compared to traditional methods. This research hassignificant implications in the field of obstetrics, paving the way foradvancements in early detection and intervention of fetal health concerns.Future work involves validating the model on a larger dataset and developing aclinical application. Ultimately, we anticipate that our research willrevolutionize the assessment and management of fetal health, contributing toimproved healthcare outcomes for expectant mothers and their babies.

Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning