Fetal Health Prediction using Machine Learning

Fetal health monitoring during pregnancy is crucial for ensuring a healthy outcome for both the mother and the baby. Traditional methods of monitoring fetal health involve routine check-ups and tests, which can sometimes miss subtle signs of fetal distress. However, recent advancements in machine learning techniques have shown promising results in accurately predicting fetal well-being and identifying potential health risks. In this paper, we review the state-of-the-art machine learning techniques used for fetal health monitoring, including both supervised and unsupervised learning methods. We discuss the challenges associated with collecting and processing fetal health data, and how these challenges can be overcome using machine learning. We also highlight the benefits of using machine learning in fetal health monitoring, such as early detection of fetal distress and personalized care plans for expectant mothers. Furthermore, we present some of the recent research efforts in the area of fetal health monitoring, which have demonstrated the effectiveness of machine learning models in predicting fetal distress and improving the accuracy of fetal heart rate monitoring. Finally, we discuss the future directions for research in this field and the potential impact of machine learning on improving fetal health outcomes.

KEYWORDS: K-Nearest neighbor, Lasso regression, Random forest, Ada boost, Cat boost and XG boost.