Paper Contents
Abstract
Fetal health classification utilizing machine learningis to study, accurately predicting the fetal condition duringpregnancy. Timely identification of fetal health issues is ofparamount importance as it can significantly impact the overalloutcome of a pregnancy. This study proposes a methodologyfor classifying fetal health status through the utilization ofmachine learning techniques, primarily relying on ultrasoundimages and various clinical parameters. To facilitate the trainingand evaluation of our model, we will compile a substantialdataset comprising ultrasound images and pertinent clinicalcharacteristics from pregnant women. Our proposed model willleverage machine learning algorithms and image processingtechniques to categorize the fetal health status. The objectiveis to achieve a high level of accuracy in classifying fetal healthstatus, ultimately enhancing the capacity of obstetricians andgynecologists to provide improved care to expectant mothers.Index TermsMachine learning, Genetic programming, Arti-ficial neural network, Algorithms
Copyright
Copyright © 2025 Atir Akhtar Ansari. This is an open access article distributed under the Creative Commons Attribution License.
