| dc.description.abstract | The condition of fetal overgrowth, also known as macrosomia, can cause serious health
complications for both the mother and the infant. It is crucial to identify high-risk
macrosomia- relevant pregnancies and intervene appropriately. Despite this need, there are
several gaps in research related to macrosomia, including limited predictive models,
insufficient machine learning applications, ineffective interventions, and inadequate
understanding of how to integrate machine learning models into clinical decision-making.
To address these gaps, we developed a machine learning-based model that uses maternal
characteristics and medical history to predict macrosomia. Three different algorithms,
namely logistic regression, support vector machine, and random forest, were used to
develop the model. Based on the evaluation metrics, the logistic regression algorithm
provided the best results among the three. The logistic regression algorithm was chosen as
the final algorithm to predict macrosomia. The hyper parameters of the logistic regression
model were tuned using cross-validation to achieve the best possible performance. Our
results indicate that machine learning-based models have the potential to improve
macrosomia prediction and enable appropriate interventions for high-risk pregnancies,
leading to better health outcomes for both mother and fetus. By leveraging machine
learning algorithms and addressing research gaps related to macrosomia, we can potentially
reduce the health risks associated with this condition and make informed decisions about
high-risk pregnancies. | en_US |
