<![CDATA[Diabetes mellitus type 2 is a metabolic condition with a rising global prevalence. Accurate classification is crucial for proper diagnosis and management. This research reviews the literature on the application of Gradient Boosting algorithms, particularly XGBoost and LightGBM, in classifying type 2 diabetes. The review indicates that Gradient Boosting algorithms have significant potential in improving the accuracy of disease diagnosis and risk prediction. Studies examined demonstrate the ability of these algorithms to handle complex data, achieve high accuracy rates, and address class imbalance issues. Moreover, parameter optimization such as hyperparameter tuning can significantly enhance model performance. This review highlights the benefits and potential of Gradient Boosting algorithms in enhancing healthcare systems through early detection and more effective management of type 2 diabetes.]]>
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