Evaluating Machine Learning Algorithms for Early Detection of GDM

Through the use of the Gestational Diabetes Mellitus (GDM) Data Set, this research conducts an in-depth analysis of machine learning methods for the early diagnosis of GDM. The effectiveness of various algorithms is evaluated by the use of metrics such as recall, accuracy, precision, and F1 score. There are several algorithms that fall under this category, such as Logistic Regression, Long Short-Term Memory (LSTM), Gradient Boosting Classifier, Random Forest, and K-Nearest Neighbors. It is necessary to do meticulous data preparation in order to find the best models for GDM prediction. Some examples of this preparation include feature scaling and principal component analysis (PCA). Should this research prove to be effective, it has the potential to provide insight on how machine learning may be used to enhance the diagnosis and management of GDM.