Ensemble Classifier Technique to Predict Gestational Diabetes Mellitus (GDM)

Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification GDM. Machine Learning (ML) methods are found to be efficient prediction techniques significant advantage over statistical models. this view, current research paper presents ensemble ML-based GDM and classification The presented model involves three steps such as preprocessing, classification, voting process. At first, input medical data preprocessed in four levels namely, format conversion, class labeling, replacement missing values, normalization. Besides, ML models Logistic Regression (LR), k-Nearest Neighbor (KNN), Support Vector (SVM), Random Forest (RF) used for classification. addition above, RF, LR, KNN SVM classifiers integrated perform final which classifier also used. order investigate proficiency proposed model, authors extensive set simulations results examined under distinct aspects. Particularly, has outperformed classical precision 94%, recall accuracy 94.24%, F-score 94%.