<![CDATA[Diabetes mellitus is a chronic metabolic disease whose prevalence continues to increase worldwide and is often diagnosed late owing to insufficient recognition of its early symptoms. One of the major challenges in diabetes classification using machine learning is class imbalance in the dataset, which may cause the model to disproportionately favor the majority class and consequently compromise its capacity to accurately identify diabetic patients. Accordingly, this research seeks to compare the classification effectiveness of Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) algorithms combined with the Artificial Minority Oversampling Approach (SMOTE) applied to diabetes classification task. The study utilized the Pima Indians Diabetes Dataset consisting of 768 instances and 8 attributes and followed the Machine Learning Life Cycle (MLLC), including exploratory data analysis, MinMax Scaler normalization, feature selection using Mutual Information, class imbalance handling with SMOTE, model training, and evaluation using accuracy, precision, recall, F1-score, and ROC-AUC as quantitative measures. Analysis of the results demonstrates that the application of SMOTE significantly improved the performance of both models. LightGBM with SMOTE achieved the best performance, with an accuracy of 85.33%, precision of 81.55%, recall of 91.33%, F1-score of 86.16%, and ROC-AUC of 90.49%, while XGBoost with SMOTE achieved an accuracy of 83.00%, precision of 79.64%, recall of 88.67%, F1-score of 83.91%, and ROC-AUC of 90.04%. These findings demonstrate that the combination of LightGBM and SMOTE is more effective than XGBoost in detecting diabetes cases and holds considerable promise for deployment as a clinical decision-making tool in early diabetes screening.]]>
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