Abstract:
Diabetes, caused by a rise in blood glucose levels, can be detected using a variety of instruments that analyze blood
samples. Heart attacks and kidney failure are among the serious complications that can arise from untreated diabetes. Consequently,
the field of detecting and evaluating gestational diabetes requires more robust research and better learning models. The information
system for detecting diabetes in this study is based on machine learning (ML) algorithms. In the study, various machine learning
techniques are discussed, including Decision Trees (DT), Random Forest (RF), Logistic Regression (LR), Extreme Gradient
Boosting (XGBoost), and K-Nearest Neighbors (K-NN). The data was collected from the Iraqi society, mainly from the laboratory of
Medical City Hospital and the Specialises Centre for Endocrinology and Diabetes-Al-Kindy Teaching Hospital. On the basis of the
Recursive Feature Elimination approach, research has been done to enhance the prediction index. The performances of all five
algorithms are evaluated on the various measures like the Precision, Accuracy, F-Measure, Recall, Cohen Kappa, and AUC.
Accuracy is measured over correctly and incorrectly classified instances. The Results obtained show XGBoost outperforms with the
highest accuracy of 98% comparatively other algorithms. This study's findings can be inform a program for screening potential
diabetes patients.
